185165957

Docket Exhibit Number Commissioner Admin. Law Judge ORA Witness

: : : : :

A.16-09-003 ORA-1 Michael Picker Stephen C. RoscowEric Duran

OFFICE OF RATEPAYER ADVOCATES CALIFORNIA PUBLIC UTILITIES COMMISSION

TESTIMONY ON SOUTHERN CALIFORNIA EDISON’S

2016 RATE DESIGN WINDOW APPLICATION

San Francisco, California April 28, 2017

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TABLE OF CONTENTS

PAGE

I. SUMMARY AND RECOMMENDATIONS..........................................................1

II. DISCUSSION AND ORA’S PROPOSALS ............................................................3

A. TOU PERIOD DETERMINATION .....................................................................3

1. Marginal Energy Costs ....................................................................4

2. Marginal Generation Capacity Costs ...............................................5

3. Marginal Distribution Demand Costs ..............................................6

4. ORA’s TOU Recommendations ......................................................7

5. Results of ORA’s Adjustments on TOU Period Determination ................................................................................10

B. RATEPAYER BILL IMPACTS .........................................................................12

C. TOU IMPLEMENTATION PLAN ....................................................................16

D. SCE’S ALTERNATIVE CPP PROPOSAL ........................................................17

III. CONCLUSION ......................................................................................................19

APPENDIX A ............................................................................................................ 22

ATTACHMENTS ........................................................................................................ 24

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I. SUMMARY AND RECOMMENDATIONS 1

This testimony presents the Office of Ratepayer Advocates’ (ORA) assessment of 2

Southern California Edison Company’s (SCE) time-of-use (TOU) period and critical peak 3

pricing (CPP) proposals in its Rate Design Window (RDW) Application (A.)16-09-003. SCE’s 4

TOU period proposals are shown in the following table. 5

Table 1: SCE’s Existing and Proposed TOU Periods

Period  Season  Existing  SCE Proposed 

On‐Peak  Summer Weekdays: 12:00 p.m. ‐ 6:00 p.m. 

Weekdays: 4:00 p.m. ‐ 9:00 p.m. 

Mid‐Peak 

Summer Weekdays: 8:00 a.m. ‐ 12:00 p.m.; 6:00 a.m. ‐ 11:00 p.m. 

Weekends 4:00 p.m. ‐ 9:00 p.m. 

Winter  Weekdays: 8:00 a.m. ‐ 9:00 p.m. Weekdays and Weekends: 4:00 p.m. ‐ 9:00 p.m. 

Off‐Peak 

Summer Weekdays: 11:00 p.m. ‐ 8:00 a.m.; Weekends: All hours 

Weekdays and Weekends: All hours except 4:00 p.m. ‐ 9:00 p.m. 

Winter Weekdays: 9:00 p.m. ‐ 8:00 a.m.; Weekends: All hours 

Weekdays and Weekends: 9:00 p.m. ‐ 8:00 a.m. 

Super Off‐Peak  Winter  N/A Weekdays and Weekends: 8:00 am ‐ 4:00 p.m. 

SCE proposes retaining the current summer (June – September) and winter (October – 6

May) seasonal definitions. SCE’s TOU proposals in this RDW are for the non-residential 7

ratepayer classes only. However, SCE states that the TOU periods adopted in this proceeding 8

will inform default TOU periods for residential ratepayers.1 In addition, as specified in the 9

adopted settlement to the last General Rate Case (GRC) Phase 2,2 SCE’s TOU proposals in this 10

RDW will not change how revenues are allocated among ratepayer classes. 11

Regarding CPP, SCE proposes updating the non-residential CPP rates based on the 12

adopted TOU periods. Further, SCE requests to make CPP rates optional for small Commercial 13

and Industrial (C&I) ratepayers. 14

1 See footnote 69, page 46 of SCE’s Testimony in Support of its Application for Approval of its 2016 Rate Design Window Proposals, A.16-09-003. 2 Settlement adopted in Commission Decision 16-03-030. The settlement includes agreement on how revenues are supposed to be allocated among customer classes.

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The following are ORA’s findings and recommendations: 1

ORA does not object to the marginal cost values SCE 2

used to determine TOU periods. These include marginal 3

energy costs (MEC), marginal generation capacity costs 4

(MGCC) and marginal distribution demand costs 5

(MDDC). ORA finds that the allocation of these marginal 6

costs drive TOU period determination more so than the 7

marginal cost values themselves. ORA will continue its 8

analysis of marginal costs in SCE’s forthcoming GRC 9

Phase 2 for revenue allocation and rate design purposes. 10

The time-dependent marginal costs should be based on 11

2021 data rather than 2024 data. SCE intends to 12

implement the proposed TOU periods starting October 13

2018. Basing TOU periods on 2021 data would provide a 14

better reflection of marginal costs over the minimum 15

duration of the TOU periods, i.e. 2018–2023 or 2024. 16

SCE’s allocation of MGCC between peak capacity and 17

flexible ramping capacity costs at 60/40 is reasonable. 18

Flexible ramping costs should be allocated to each hour in 19

the three-hour ramping period. Further, the allocation 20

should be based on each hour’s relative ramp compared to 21

the daily ramp. 22

SCE’s determination of peak versus grid related 23

components of its marginal distribution costs for TOU 24

period determination is acceptable. ORA will assess this 25

approach in relation to the comprehensive evaluation of 26

distribution costs SCE intends to file in its forthcoming 27

GRC Phase 2. 28

Based on the above recommendations, ORA developed an 29

hourly marginal cost profile which informed the following 30

TOU structure proposal: 31

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Table 2: SCE’s and ORA’s Proposed TOU Periods

Period  Season  SCE Proposed  ORA Proposed 

On‐Peak  Summer  Weekdays: 4:00 p.m. ‐ 9:00 p.m.  Weekdays: 3:00 p.m. ‐ 8:00 p.m. 

Mid‐Peak 

Summer  Weekends 4:00 p.m. ‐ 9:00 p.m.  Weekends 3:00 p.m. ‐ 8:00 p.m. 

Winter Weekdays and Weekends: 4:00 p.m. ‐ 9:00 p.m. 

Weekdays and Weekends: 3:00 p.m. ‐ 8:00 p.m. 

Off‐Peak 

Summer Weekdays and Weekends: All hours except 4:00 p.m. ‐ 9:00 p.m. 

Weekdays and Weekends: All hours except 3:00 p.m. ‐ 8:00 p.m. 

Winter Weekdays and Weekends: 9:00 p.m. ‐ 8:00 a.m. 

Weekdays and Weekends: 8:00 p.m. ‐ 8:00 a.m. 

Super Off‐Peak  Winter Weekdays and Weekends: 8:00 am ‐ 4:00 p.m. 

Weekdays and Weekends: 8:00 am ‐ 3:00 p.m. 

SCE’s new TOU period proposals disproportionately and 1

adversely impact smaller non-residential ratepayers based 2

on ratepayer bill impact analyses. SCE should consider 3

instruments such as a balanced payment plan (BPP) and 4

targeted outreach and education to mitigate bill increases 5

for groups most impacted by these TOU periods. 6

SCE’s two-phase TOU implementation plan is reasonable 7

in light of planned billing system upgrades. 8

The Commission should adopt SCE’s proposal to make 9

CPP an optional rate for small commercial ratepayers. 10

II. DISCUSSION AND ORA’S PROPOSALS 11

A. TOU Period Determination 12

In December 2015, the Commission opened rulemaking (R.) 15-12-012 (TOU OIR) with 13

the intent of developing “the principles, methodologies, and data sources needed to identify 14

TOU periods that better reflect actual and near-term expected electricity supply and demand.”3 15

The TOU OIR Decision (D.) 17-01-006 adopted “a marginal cost-based method, rather than a 16

load-based method, for purposes of the data requirements for determining TOU periods.”4 17

Further, the Commission made clear that it would not adopt a specific process for incorporating 18

3 R.15-12-012, pp. 2. 4 D.17-01-006, pp. 26.

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distribution peaks into base TOU periods and that this process should be explored in future IOU-1

specific rate cases.5 Finally, the Commission endorsed the use of flexible ramping costs in 2

determining TOU periods; however, just as in the case of distribution costs, it did not provide 3

specific methods as to how this should be done.6 4

In this RDW, SCE does propose a marginal cost method to inform its TOU period 5

proposal. SCE uses a combination of time-dependent marginal costs, including marginal energy 6

costs (MEC), marginal generation capacity costs (MGCC), and marginal distribution demand 7

costs (MDDC) to develop hourly “total marginal costs”. SCE’s methods for determining these 8

marginal costs are similar to past proposals in GRC Phase 2 cases. ORA focused on validating 9

these marginal costs and their impact on TOU determination by testing the sensitivity of these 10

values on TOU periods.7 ORA further assessed and tested SCE’s assumptions related to the 11

allocation of these marginal costs across all hours of the year for the purposes of developing 12

TOU periods. The following subsections describe SCE’s methods and ORA’s results from its 13

sensitivity analysis using SCE’s RDW tool. The last two subsections describe ORA’s method 14

for developing hourly marginal costs and how the TOU period proposal, as shown in Table 2, 15

was derived. 16

1. Marginal Energy Costs 17

SCE determines hourly MECs using the production cost modelling software PLEXOS. 18

ORA obtained information on SCE’s MEC model through Data Request.8 ORA ran SCE’s 19

model in PLEXOS to validate SCE’s results. ORA’s MEC results were fairly similar to SCE’s. 20

Differences in the results are immaterial and likely caused by varying computer specifications 21

and PLEXOS solver differences.9 22

5 Ibid., pp. 29-31. 6 Ibid., pp. 31. 7 ORA used SCE’s rate design tool, filed as “A1609xxx-SCE Rate Design Window Tool” on September 1, 2016. 8 ORA-SCE-002 9 In PLEXOS, SCE used the CPLEX 12.6.1.0 solver while ORA used Xpress-MP 28.01.04 solver. This could technically lead to varying results as well as the differences in computer processing power specifications.

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ORA modeled SCE’s proposed summer months as well as the shoulder months (May and 1

October). Initial results indicate that there are slight differences in the mean MEC values when 2

aggregated on a monthly basis. However, ORA does not object to SCE’s values of MECs for the 3

purposes of TOU determination. As was discussed at the pre-hearing conference (PHC) of this 4

proceeding, the appropriate forum for marginal cost determination would be SCE’s GRC Phase 5

2.10 Further, as discussed later on, ORA notes the allocation of these marginal costs drive TOU 6

period definitions more than the marginal cost values themselves. ORA will continue to review 7

SCE’s MEC model in the upcoming GRC Phase 2 case in relation to revenue allocation. 8

2. Marginal Generation Capacity Costs 9

Similar to SCE’s last GRC Phase 2, MGCC is based on the deferral value of a new build 10

combustion turbine (CT) proxy resource.11 However, an important distinction in this case is that 11

the new CT built is assumed to provide flexible ramping capacity in addition to peaking 12

capacity. 13

To determine the portion of MGCC related to flexible ramping capacity costs, SCE 14

proposes a “proxy methodology.”12 This proposed method incorporates guidance from the 15

California Independent System Operator’s (CAISO) Flexible Resource Adequacy Criteria Must 16

Offer Obligation (FRAC-MOO) mechanism. First, SCE determines the ratio between the 17

maximum three-hour CAISO net load ramp relative to the maximum CAISO peak load. 18

Following such analysis, SCE finds that approximately 40% of the MGCC value of $147.2613 19

per kW-year should be assigned to flexible ramping needs and the remaining 60% to peak 20

capacity demand. 21

10 A.16-09-003, 12/8/16 PHC Reporters’ Transcript pp. 37 – 38. 11 SCE testimony, footnote 49, pp. 20-21. 12 SCE testimony, footnote 49, pp. 32. 13 ORA does not endorse the $147 kW-year MGCC costs. However, as explained later on in this testimony, no alternative value is proposed as sensitivity analysis using a much smaller value did not yield significant changes to the allocation of hourly marginal costs. Further, it is important to note that this proceeding is to address how hourly marginal costs inform TOU period determination and not on the value of marginal costs themselves. ORA will conduct an extensive review on SCE’s marginal cost proposals in their GRC Phase 2.

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SCE then allocates the flexible ramping capacity costs to hours by weighting the 1

maximum three-hour upward ramp for every day of the year based on their relative size. For 2

instance, an expected three-hour ramp of 18,718 MW on December 1, 2024 is weighted at 2.2 3

times a three-hour ramp of 8,495 MW on January 1, 2024, and will therefore be allocated 2.2 4

times more of the total flexible capacity ramping costs.14 Intra-day ramping allocations are 5

based on SCE’s judgement that a 30/70 split between the 2nd and 3rd hours of the ramp will send 6

the appropriate price signal to ratepayers with the intent to flatten steep ramps.15 The remainder 7

of MGCC, considered peak capacity cost, is allocated to each hour of the year based on the 8

relative Loss of Load Expectation (LOLE) method.16 9

3. Marginal Distribution Demand Costs 10

For distribution, SCE proposes incorporating the time-dependent distribution marginal 11

costs by determining the allocation of distribution costs which are peak- and grid-related 12

components. The peak-related components are regarded as the time-dependent costs. These 13

components are identified by bifurcating design demand costs using what SCE calls the 14

“NERA/FERC17 method” because it depends upon SCE’s FERC-filed costs. This method 15

categorizes peak- and grid-related costs based on recorded investments in SCE’s FERC Form 1 16

filing.18 SCE identifies approximately 60% of its distribution costs as peak-related, and 17

therefore time-dependent. This allocation is then applied to hourly distribution marginal costs19 18

to obtain the distribution cost component of the total marginal costs. SCE proposes this method 19

be used on an interim basis, as a more comprehensive evaluation will be provided in their 20

14 SCE’s “MGCC Workpaper” obtained in ORA Data Request 2. 15 SCE does not allocate any of the flexible ramping capacity costs to hour 1 of the ramp. 16 See SCE testimony pages 23-27 for discussion on the LOLE method. 17 National Economic Research Associates (consulting firm) and the Federal Energy Regulatory Commission. 18 Ibid., pp. 35. 19 Distribution costs are allocated to individual hours using a Peak Load Risk Factor (PLRF) methodology. See SCE testimony pages 38-42 for discussion on the PLRF method.

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forthcoming GRC Phase 2 proceeding.20 ORA agrees with using this method on an interim basis 1

until SCE files a more detailed proposal, at which point ORA will investigate its reasonableness. 2

4. ORA’s TOU Recommendations 3

SCE’s RDW tool was built with the intention of allowing alternate assumptions.21 ORA 4

used the tool to test the sensitivity of varying marginal cost assumptions. The results of this 5

analysis showed that, under varying marginal costs valuations, the TOU periods remained 6

consistent with SCE’s proposal. Even when the marginal costs values change significantly, the 7

overall spread in costs throughout the year remains nearly unchanged.22 As discussed in the 8

following subsections, ORA finds that most important element for determining TOU periods is 9

the method used to create hourly cost profiles for allocating costs. Based on the following 10

recommendations, ORA inspected the resulting hourly marginal cost profiles and searched the 11

most optimal TOU period structures that balanced data relevancy, the TOU period’s 12

effectiveness in capturing the appropriate costs,23 and a gradual change from current TOU 13

periods. Heat maps and regression results of the following recommendations are included in 14

Attachments A and D. 15

a) 2021 as the Reference Year for Marginal Costs 16

ORA proposes the marginal cost analysis used for the development of TOU periods be 17

based on 2021 as opposed to SCE’s proposal of 2024. The Commission, in the TOU OIR 18

Decision states that: 19

TOU periods should be developed using forward-looking 20

data, forecasted at least three years after the TOU period will 21

go into effect, so that the resulting TOU periods will be 22

stable. Any subsequent re-evaluation of those periods should 23

20 SCE testimony, pp. 34. 21 This is evident as the tool allows for various inputs to marginal cost assumptions. 22 For example, Attachment B compares the heat maps of using an MGCC value of 147.26 per kW-year (SCE’s proposal) with one using a $30 per kW-year value. 23 ORA used performance measures similar to SCE’s. This is discussed in SCE testimony, pp. 69-72.

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be done in utility-specific proceedings, either in GRC phase 2 1

or RDW proceedings.24 2

SCE’s proposal of using 2024 data in their analysis is over five years ahead of their 3

proposed implementation date of October 2018. While the Commission decision does not 4

explicitly disallow the reference year to be five years in advance, using a reference year so far 5

into the future could increase likelihood of forecasting errors in the development of TOU 6

periods. The forecasting errors associated with a 2021 forecast would likely be smaller than 7

those associated with 2024. 8

The Commission did express that subsequent revisions could be conducted in either the 9

GRC Phase 2 or RDW proceedings. Currently, the IOUs are presenting proposals for dead band 10

tolerance ranges for determining when changes to TOU periods should occur more frequently 11

than five-year intervals.25 The outcome will clarify how SCE can use the procedural venues 12

available to update TOU periods. This will avoid SCE committing to a TOU period structure 13

which relies on data far ahead of the actual TOU implementation date. 14

b) Peak/Flex MGCC Allocation 15

SCE recommends a 60/40 split of MGCC between peak capacity and flexible ramping 16

capacity (flex) costs respectively. ORA used SCE’s RDW tool which contains CAISO net load 17

data to validate SCE’s allocation of the peak capacity to flexible ramping capacity allocation. 18

ORA used the following equations, as presented in SCE’s testimony, to determine the 19

appropriate allocation between the two. 20

∗

1 ∗

Using 2021 data from the RDW tool, ORA obtained a maximum three-hour net load 21

ramp of 15,044 MW and a maximum peak of 35,527 MW. Using these input to solve the above 22

24 D.17-01-006, pp.46. 25 See SCE Advice Letter 3581-E.

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equations results in an approximate allocation of 58% to peak capacity costs and 42% to flexible 1

ramping capacity costs. Though SCE determined its 60/40 split based on a different dataset,26 2

ORA does not contest the use of a 60/40 split as it arrived at similar values for the purposes of 3

this proceeding. 4

c) Flex Allocation within Ramping Hours 5

Flexible ramping costs, calculated as 40% of the MGCC, are then allocated to the daily 6

ramping hours. ORA’s ramping hours were identified using the same method that SCE used in 7

its MGCC workpaper. This involved identifying the largest consecutive three-hour ramp within 8

every day of the year. The ending hour of this ramp was defined as the third hour of the ramp. 9

SCE’s proposal allocates 70% of the flexible ramping capacity costs to this third hour, while the 10

remaining 30% is allocated to the second hour in the ramp. 11

ORA used the CAISO net load data found in SCE’s RDW tool to determine the ramping 12

periods. ORA then determined the allocation of flex costs within each daily ramping hour by 13

developing weights based on each hour’s contribution to the total three-hour ramp. For example 14

if the total three-hour ramp on a given day is 15,000 MW, and the first hour’s ramp is 6,000 15

MW, then the first hour’s calculated weight would be 0.4.27 16

Finally, ORA normalized to the total ramp hours throughout the year. The following 17

equation shows the weighting and normalization process. 18

Where Total Ramp = Sum of all daily 3-hour ramps

The following figure illustrates the identification of the largest three-hour ramp, and the 19

assignment of weights to each hour based on the actual ramp. 20

26 In Data Request ORA-SCE-002, requesting support for SCE’s MGCC calculation, ORA obtained the dataset on which SCE based its FLEX allocation. Further, this allocation is based on rounded allocation of 64.5/35.5 in the actual workpaper. 27 Contrast this with SCE’s proposal which would not allocate any value to the first hour’s ramp. Instead only the second and third hours would be allocated flexible ramping capacity cost without any consideration of how much each hour’s relative impact is to the total three-hour ramp.

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Figure 1: Illustrated Example of Daily Ramping Period

This method allows certain hours in the ramp to be weighted more than the third hour, 1

which SCE proposes be fixed at 70%. This is important because steep ramps may not always 2

occur within the third hour. Further, this method allows for days to reflect different ramping 3

needs. For example, ramping in the spring months may be different than in the summer 4

months. SCE’s method does not allow for this level of distinction. Attachment C shows the 5

results in the form of a heat map. 6

5. Results of ORA’s Adjustments on TOU Period Determination 7

ORA conducted performance measure analyses using hourly total marginal costs derived 8

from ORA’s flex allocation proposals and 2021 data.28 The results indicate that a 3:00 P.M. to 9

8:00 P.M. period29 performs nearly as well as the 4:00 P.M. to 9:00 P.M. period when capturing 10

the top 20 and 100 hours. It also represents a more gradual change from the existing TOU 11

28 Performance analyses were conducted using SCE’s methods as described in their RDW Testimony, pp.69 – 72. ORA obtained this analysis in ORA Data Request ORA-SCE-005. 29 For the weekday summer on-peak, weekend summer mid-peak, and winter mid-peak periods.

 6,000

 7,000

 8,000

 9,000

 10,000

 11,000

 12,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

MW

hour ending (HE)

Largest 3‐hour RampHE18‐HE16 

Ramp of HE 16

Ramp of HE 17 

Ramp of HE 18 

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periods, mitigating bill impacts for lower-usage ratepayers. Bill impacts are discussed in the 1

next section. The following table, similar to SCE’s Table IV-10, compares various TOU period 2

scenarios’ abilities to capture the top 20 and 100 hours under ORA’s marginal cost assumptions 3

and using 2021 data. 4

Table 3: Highest-Cost Hours in Various Summer On-Peak Periods

under ORA’s Marginal Cost Allocations

      Weekday Peak Period

     

3 p.m. to 8 p.m.

(ORA)

4 p.m. to 9 p.m. (SCE)

4 p.m. to 8 p.m.

4 p.m. to 10 p.m.

5 p.m. to 9 p.m.

5 p.m. to 10 p.m.

Noon to 6 p.m.

2 p.m. to 8 p.m.

Top 20 hours

Number of Hours Captured

17 18 16 18 15 15 7 17

% Captured 85% 90% 80% 90% 75% 75% 35% 85%

Top 100

hours

Number of Hours Captured

78 80 69 80 75 75 41 80

% Captured 78% 80% 69% 80% 75% 75% 41% 80%

The following regression analysis shows that using 2021 data and ORA’s marginal cost 5

analysis, the 3 p.m. to 8 p.m. performs better at capturing costs in the appropriate TOU period 6

than SCE’s proposal.30 The independent variable, total marginal costs, is regressed against the 7

dependent binary variables defining seasonal and TOU periods. Further a “Top 20” variable is 8

included to represent whether the total marginal cost in that hour is included in the highest cost 9

20 hours of the year. Attachment D shows the detailed results, while attachment E contains a 10

description of SCE’s regression method. 11

30 Attachment E contains SCE’s response to ORA’s fifth Data Request. In this response SCE describes the regression process. ORA included in its regression the same variables as SCE’s 4 to 9 p.m. regression. ORA’s regression is specific to the 3 to 8 p.m. on-peak period, uses 2021 data marginal cost data, and incorporates the allocation of flexible ramping and peak capacity costs method as described earlier. It is important to note that the regression differs from the Top 20/100 hour analysis in that it is inclusive to all hours of the year. Therefore this regression analysis can be considered broader than the one that is looking at the top 20/100 high cost hours. ORA’s 3 to 8 p.m. proposal performs better than SCE’s 4 to 9 p.m. proposal at grouping marginal costs into appropriate TOU periods. Further it is important to note that the result of these regression analyses shows that, as SCE concludes in its response, “there are several scenarios that yield quite comparable results and therefore, customer considerations may rightfully be a deciding factor.” ORA’s choice of 3 to 8 p.m. on-peak proposal would better accommodate customer considerations in that it provides a more gradual change from the current 12 to 6 p.m. on-peak period.

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Table 4: Regression Statistics

SCE ORA

Period 4:00 P.M. to 9:00 P.M. 3:00 P.M. to 8:00 P.M.

Multiple R 0.6283 0.6868

R Square 0.3948 0.4717

Adjusted R Square 0.3944 0.4713

Sum of Squared Errors 412.2216 276.8889

Standard Error 0.2170 0.1778

Observations 8760 8760

SCE’s 4 p.m. to 9 p.m. proposed on-peak period, while performing relatively well in 1

TOU analysis, can be shifted one hour earlier, at both the start and end times, and perform 2

equally as well or better in these analyses. The added benefit to a 3 p.m. to 8 p.m. on-peak 3

period is that it would provide a more gradual movement of the current 12 p.m. to 6 p.m. on-4

peak period. This alone could potentially help mitigate ratepayer frustrations and increase their 5

ability to respond to new TOU periods. Therefore, ORA recommends the 3 p.m. to 8 p.m. on-6

peak period. 7

B. Ratepayer Bill Impacts 8

In addition to testing the sensitivity of marginal cost allocation assumptions, ORA 9

assessed SCE’s TOU proposal’s bill impacts. The following table shows the average bill impact 10

for small commercial ratepayers who benefit from SCE’s TOU proposal and those who do not.31 11

31 ORA focuses its bill impact analysis to small commercial ratepayers with maximum demands of less than 20 kW, i.e. ratepayers on the TOU-GS-1 tariff.

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Table 5: Categorized Bill Impacts

Period  Bill Category  Count  Percentage Avg. Change from OAT32  Avg. Demand (kW)

Annual Decrease 21,415 39% -2.0% 6.75

Increase 33,132 61% 1.4% 3.78

Summer Decrease 7,058 38% -3.6% 7.32

Increase 11,314 62% 2.6% 3.89

Winter Decrease 14,357 40% -1.2% 6.47

Increase 21,818 60% 0.7% 3.72

It is evident that larger ratepayers (those with higher average kW usage) benefit from 1

SCE’s TOU proposal. Appendix C of SCE’s testimony, which shows the bill impacts of 2

ratepayers on the TOU-GS-1 tariff on an annual basis, shows results similar to those in the table 3

above.33 4

ORA extended SCE’s analysis by conducting a monthly bill impact analysis to determine 5

whether special attention should be paid to certain months. The following table compares the 6

percentage of ratepayer bills, which fall above or below a 1% and 5% bill impact. 7

32 Otherwise Applicable Tariff – in this case bills under the current TOU periods. 33 SCE’s analysis distinguishes between ratepayers on TOU-GS-1-A and TOU-GS-1-B, while ORA’s does not for simplicity.

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Table 6: Bill Impacts by Month, Percentage of Bills within Impact Group

1% Increase

1% Decrease

5% Increase

5% Decrease

Annual 26% 23% 3% 4%

January 20% 19% 0% 0%

February 19% 18% 0% 0%

March 14% 19% 0% 0%

April 10% 20% 0% 0%

May 11% 17% 0% 0%

June 42% 32% 8% 11%

July 40% 33% 8% 12%

August 48% 28% 9% 9%

September 46% 30% 10% 11%

October 14% 21% 0% 0%

November 21% 16% 0% 0%

December 19% 20% 0% 0%

On an annual basis, 97% would see less than a 5% bill increase while 3% would see a 5% 1

or more bill increase. In addition, there is a fairly equal proportion of ratepayer bills, which face 2

a 1% bill increase or more (26%) compared to a 1% bill decrease or more (23%). These two 3

measures indicate that on an annual basis, there appears to be an even distribution of both 4

positive and negative bill impacts of the new TOU periods.34 However when looking at the 5

summer months, this distribution tends to depart in favor of negative bill impacts outweighing 6

positive ones. For example, in August, SCE’s TOU proposal would have 46% of ratepayer bills 7

increasing by 1% or more, while 28% of bills would decrease by 1% or more. This is a slight 8

offset from the spring months of March through May in which there are more bills that decrease 9

than increase. Finally, looking at the number of ratepayer bills which either increased or 10

decreased by 5% throughout the course of the year, there are more months where bills decreased 11

than increased. 12

34 While comparing table 5 with table 6 may seem to show contradictory information on the number of bills impacted both positively and negatively, this is because table 6 is looking only at bills which experience a 1% or larger increase or decrease. Table 5 does not make this +/- 1% distinction and thus results in different numbers. Based on Table 5, results would indicate that more bills fall in the 0-1% bill increase group than the 0-1% bill decrease group.

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To obtain a more granular look at how much bills would change as a result of SCE’s 1

TOU proposal, ORA grouped bill impacts into narrower percentage categories. Figure 2 shows 2

the result of this on a monthly basis. 3

Figure 2: Categorized Bill Impacts by Month

The chart shows that the most prevalent group throughout the year are bills, which 4

increase by 0 to 5% (represented by the light green line). The next prevalent group are bills, 5

which decrease by 0 to 5% (light blue), as this group typically makes up 25 to 40% of all bills 6

throughout the year. The summer bill impacts are the worst for the 5 to 10% increase and 7

decrease groups (red and orange lines respectively). Together these groups make up roughly 8

20% of all bills, split nearly evenly, for the months of June through September. The following 9

table, which contains the average maximum demand for these groups in the summer months, 10

shows that the beneficiaries to the TOU period change tend to be larger ratepayers than those 11

who experience bill increases. 12

185165957 16

Table 7: Maximum Demand for Summer Months by Bill Impact Group

Bill Impact Group

Average Maximum Demand by Summer Month (kW)

June July August September

5% to 10% 4.21 3.94 4.31 4.05

0% to 5% 3.68 3.65 4.02 4.01

-5% to 0% 6.74 6.79 7.10 7.09

-10% to -5% 8.09 8.06 8.35 8.53

ORA is concerned that SCE’s TOU period proposal will disproportionately impact 1

smaller ratepayers who may lack the resources to appropriately shift load to mitigate on-peak 2

usage. SCE should consider offering balanced payment plans to these ratepayers in order to 3

mitigate potential ratepayer frustration with the TOU period shift. When creating outreach plans, 4

SCE should provide more support to its smaller non-residential ratepayers who are more 5

adversely impacted. 6

C. TOU Implementation Plan 7

In this RDW, SCE proposes a two-phased approach to implementing TOU rates for its 8

non-residential ratepayers. The first phase would implement the revised TOU periods and rates 9

using the revenue allocation and marginal costs from its 2015 GRC Phase 2 settlement. This 10

first phase would occur on October 1, 2018. The second phase would update TOU rates based 11

on the Commission-adopted revenue allocation and marginal costs determined in SCE’s 2018 12

GRC Phase 2. The second phase is expected to occur in January 2019. Even though the second 13

phase would only change the rates and not TOU periods, ORA is concerned that frequent rate 14

changes in a short timeframe is likely to cause ratepayer confusion, frustration, and potentially 15

overwhelm SCE’s information technology (IT) and billing system. ORA, however, does not 16

oppose the two-phase approach for the reasons described below. 17

In its GRC Phase 1, SCE requests substantial funding asserting that it needs to replace its 18

aging IT and billing system. SCE notes that it may have to freeze its billing system in 2019 to 19

ensure a successful transition from the legacy system.35 The freeze would force a delay to any 20

35 “SOUTHERN CALIFORNIA EDISON COMPANY’S (U 338-E) NOTICE OF EX PARTE COMMUNICATION” distributed to the service list of this proceeding on November 28, 2016.

185165957 17

rate design changes; however during the freeze, SCE could still implement rate changes. In other 1

words, SCE’s proposed two-phased implementation approach would coordinate with its planned 2

billing system freeze. Therefore, if SCE is able to successfully implement the TOU period 3

changes for non-residential ratepayers prior to the billing system freeze in 2019, it could still 4

implement rate changes based on its 2018 GRC Phase 2 decision. 5

ORA considered recommending timing the implementation of TOU periods to coincide 6

with the implementation of GRC Phase 2 rates in order to consolidate the bill impacts. However, 7

because of the planned billing system freeze, this proposal would likely cause further delays to 8

the implementation of new TOU periods. This is because the implementation of GRC Phase 2 9

rates would likely occur during 2019 billing system freeze, rendering implementation of TOU 10

periods, a significant rate design change, infeasible. Further, SCE has indicated that changes 11

could not be made until the upgraded billing system has “stabilized,” potentially up to 18 12

months after the initial freeze date.36 Therefore, the implementation of these TOU periods could 13

potentially be delayed until mid-2020. To avoid extensive delays in the implementation of TOU 14

periods, ORA agrees with this phased approach. 15

There is still reason, however, to believe that these rate changes may cause undue 16

ratepayer frustration and confusion. From ORA’s bill impact analysis, it seems that the most 17

obvious group of non-residential ratepayers most sensitive to TOU changes are ratepayers with 18

low average demands within the small commercial class.37 ORA recommends SCE’s two-19

phased implementation approach with the caveat that SCE focus its communication and 20

outreach on the lowest-usage small commercial ratepayers, for example, those with 21

approximately less than 5 kW of maximum demand in the summer months as shown in Table 7. 22

D. SCE’s Alternative CPP Proposal 23

Decision (D.) 13-03-031 ordered that CPP rates be the default for all non-residential 24

ratepayers, including small C&I ratepayers. The last GRC Phase 2 decision, D.16-03-030, 25

further directed SCE to create CPP rates that coincide with the revised TOU periods for C&I 26

36 SCE TOU timeline presentation to the Commission during the R.12-06-013 workshop on 3/7/17. 37 Specifically ratepayers served by the TOU-GS-1 schedule.

185165957 18

with less than 200 kW and large Agricultural and Pumping (A&P) ratepayers in this RDW. In 1

addition to filing a compliance proposal, SCE proposes an alternative which would allow opt-in 2

CPP for small C&I ratepayers38 instead of default CPP.39 3

SCE’s main argument for making small C&I CPP rates optional is the relatively small 4

demand reductions shown in the ex post load impacts of PG&E’s default CPP program. SCE 5

cites a Nexant report,40 which shows a 1.5 MW reduction in aggregate demand for PG&E small 6

C&I ratepayers with demands less than 20 kW. From this result, SCE estimates an aggregate 7

load reduction of 1.3 MW or 4.5% of its estimated total CPP load. This also amounts to 0.09% 8

of SCE’s total demand response (DR) portfolio.41 Further, SCE argues that the Commission 9

should focus its efforts on other energy efficiency and DR programs which integrate into the 10

CAISO market. 11

SCE’s request to move from default to optional CPP is comparable to and consistent with 12

the Commission’s findings on the Peak Time Rebate (PTR) program. In that case, the 13

Commission Staff found that, for the participants in the SCE PTR, “95 percent of all incentives 14

were paid to ratepayers who either were not expected to or did not reduce load significantly.”42 15

The Commission used the lack of load reduction to decide that the program should be offered as 16

an optional rather than default program.43 Similarly, PG&E’s CPP program indicates there is 17

lack of load reduction. SCE’s proposal to give ratepayers the choice to opt in CPP rather than 18

default is reasonable. 19

38 Those with demand less than 20 kW. 39 D.16-03-030 adopted a settlement which required SCE to file this RDW and align the implementation of default CPP to coincide with the implementation of the revised standard TOU periods considered in this proceeding. D.13-03-031 adopted default CPP for ratepayers in the GS-1 rate group. 40 “2015 Load Impact Evaluation of California’s Statewide Nonresidential Critical Peak Pricing Program” 41 SCE Testimony, p. 105. 42 D.13-07-003, p.25. 43 Finding of Fact, 27 D.13-07-003.

185165957 19

III. CONCLUSION 1

ORA recommends the Commission adopt ORA’s TOU period proposal for the non-2

residential class, which mirrors the SCE proposal with the exception that the weekday summer 3

on-peak period, weekend summer mid-peak, and winter mid-peak period be from 3 p.m. to 8 4

p.m. as opposed to 4 p.m. to 9 p.m. Further, ORA recommends that SCE focus its ratepayer 5

outreach plans on those ratepayers most adversely impacted by the TOU period change, the 6

smallest ratepayers in the small C&I class. Additionally, SCE should offer a balanced payment 7

plan for these ratepayers in order to mitigate burdensome summer bills. Finally, ORA supports 8

SCE’s TOU implementation plan and the alternative CPP proposal.9

185165957

APPENDIX A

WITNESS QUALIFICATIONS

185165957

QUALIFICATIONSAND PREPARED TESTIMONY

OF

ERIC DURAN

Q1. Please state your name, business address, and position with the California Public Utilities Commission (Commission).

A1. My name is Eric Duran and my business address is 505 Van Ness Avenue, San Francisco,

California. I work in the Electricity Pricing and Customer Programs Branch of the Office of Ratepayer Advocates (ORA) as a Regulatory Analyst and am the project coordinator for this case.

Q2. Please summarize your education background and professional experience. A2. I received my Master of Science Degree in Agricultural and Resource Economics from the

University of California, Davis in 2013. I received a Bachelor of Science Degree in Environmental Science with a concentration in Economics from the University of California, Riverside in 2011. I have been employed by ORA since November 2013. I have sponsored testimony before the Commission in the past concerning rate design and sales forecasting for both electricity and water utilities.

Q3. What is your responsibility in this proceeding? A3. I am responsible for all ORA testimony regarding SCE’s Application (A.)16-09-003. Q4. Does this conclude your prepared direct testimony? A4. Yes, it does.

185165957

ATTACHMENTS

185165957

Attachment A: SCE and ORA Marginal Cost Heat Maps

HE

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1.044

.043.043

.043.043

.043.045

.044.041

.039.039

.038.035

.035.038

.053.166

.131.083

.067.056

.052.048

.046

2.044

.043.043

.043.043

.044.045

.043.041

.039.039

.039.038

.038.039

.043.108

.150.112

.069.060

.053.048

.045

3.043

.042.041

.041.042

.042.043

.041.038

.036.029

.026.025

.024.031

.036.058

.101.155

.102.068

.059.054

.045

4.042

.041.041

.041.042

.042.042

.037.022

.023.024

.019.016

.026.027

.031.039

.059.127

.125.071

.053.048

.043

5.043

.041.040

.041.041

.042.040

.035.023

.027.029

.032.031

.033.033

.035.038

.045.087

.134.084

.055.049

.044

6.043

.042.041

.041.041

.041.039

.031.021

.029.033

.033.030

.034.035

.037.044

.055.081

.151.077

.067.051

.044

7.046

.043.042

.041.041

.041.039

.034.030

.034.049

.054.055

.046.052

.055.065

.066.081

.123.094

.075.061

.049

8.049

.044.042

.042.042

.042.041

.037.032

.033.036

.039.041

.044.054

.057.092

.085.135

.159.114

.075.059

.048

9.046

.043.042

.041.041

.042.042

.040.035

.033.037

.039.042

.045.053

.061.179

.130.315

.178.086

.066.055

.046

10.043

.040.041

.040.040

.041.041

.040.035

.035.032

.032.035

.036.037

.040.089

.125.129

.080.064

.053.046

.043

11.042

.041.041

.040.040

.041.042

.040.037

.030.033

.032.035

.036.038

.074.177

.113.073

.066.056

.050.045

.043

12.044

.043.042

.042.042

.044.044

.043.041

.040.040

.039.039

.039.039

.082.170

.131.075

.072.064

.057.050

.045

OR

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1.050

.049.048

.048.048

.049.054

.058.045

.039.040

.037.035

.032.033

.043.086

.161.088

.073.064

.059.056

.051

2.050

.048.048

.049.048

.049.051

.048.044

.037.041

.038.033

.036.041

.046.063

.111.146

.073.064

.058.058

.052

3.049

.046.047

.047.047

.047.048

.046.038

.021.014

.007.008

.007.012

.030.058

.120.125

.086.066

.060.059

.051

4.048

.046.046

.046.046

.047.046

.041.029

.019.016

.015.009

.009.014

.023.038

.084.143

.060.066

.061.057

.050

5.048

.045.045

.045.046

.047.045

.035.019

.022.018

.020.018

.012.015

.028.041

.086.127

.069.068

.062.055

.049

6.051

.047.047

.047.046

.047.045

.034.019

.021.027

.027.024

.032.039

.042.048

.094.135

.084.074

.075.065

.054

7.054

.049.048

.047.047

.047.045

.037.028

.035.037

.039.041

.044.047

.050.054

.112.172

.092.105

.079.070

.055

8.057

.050.049

.049.048

.049.049

.043.037

.036.036

.040.042

.044.048

.073.114

.141.150

.143.118

.086.073

.059

9.054

.049.049

.048.047

.048.048

.045.033

.021.028

.034.037

.044.049

.066.078

.195.683

.286.147

.083.067

.056

10.052

.048.048

.048.048

.049.050

.047.040

.036.030

.036.040

.042.042

.046.079

.159.129

.093.070

.062.058

.051

11.050

.049.048

.048.048

.049.051

.048.044

.040.037

.035.032

.036.041

.067.139

.173.077

.072.063

.061.056

.051

12.051

.050.048

.049.049

.049.051

.049.045

.040.042

.042.041

.042.043

.046.111

.219.075

.076.067

.064.061

.053

SC

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185165957

Attachment B: Marginal Generation Cost Comparison Heat Maps

HE

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1.050

.049.048

.048.048

.049.054

.058.045

.039.040

.037.035

.032.033

.043.086

.161.088

.073.064

.059.056

.051

2.050

.048.048

.049.048

.049.051

.048.044

.037.041

.038.033

.036.041

.046.063

.111.146

.073.064

.058.058

.052

3.049

.046.047

.047.047

.047.048

.046.038

.021.014

.007.008

.007.012

.030.058

.120.125

.086.066

.060.059

.051

4.048

.046.046

.046.046

.047.046

.041.029

.019.016

.015.009

.009.014

.023.038

.084.143

.060.066

.061.057

.050

5.048

.045.045

.045.046

.047.045

.035.019

.022.018

.020.018

.012.015

.028.041

.086.127

.069.068

.062.055

.049

6.051

.047.047

.047.046

.047.045

.034.019

.021.027

.027.024

.032.039

.042.048

.094.135

.084.074

.075.065

.054

7.054

.049.048

.047.047

.047.045

.037.028

.035.037

.039.041

.044.047

.050.054

.112.172

.092.105

.079.070

.055

8.057

.050.049

.049.048

.049.049

.043.037

.036.036

.040.042

.044.048

.073.114

.141.150

.143.118

.086.073

.059

9.054

.049.049

.048.047

.048.048

.045.033

.021.028

.034.037

.044.049

.066.078

.195.683

.286.147

.083.067

.056

10.052

.048.048

.048.048

.049.050

.047.040

.036.030

.036.040

.042.042

.046.079

.159.129

.093.070

.062.058

.051

11.050

.049.048

.048.048

.049.051

.048.044

.040.037

.035.032

.036.041

.067.139

.173.077

.072.063

.061.056

.051

12.051

.050.048

.049.049

.049.051

.049.045

.040.042

.042.041

.042.043

.046.111

.219.075

.076.067

.064.061

.053

$147.26 pe

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1.050

.049.048

.048.048

.049.051

.052.045

.039.040

.037.035

.032.033

.043.059

.090.076

.073.064

.059.056

.051

2.050

.048.048

.049.048

.049.051

.048.044

.037.041

.038.033

.036.041

.044.051

.070.086

.073.064

.058.058

.052

3.049

.046.047

.047.047

.047.048

.046.038

.021.014

.007.008

.007.012

.030.042

.062.072

.075.066

.060.059

.051

4.048

.046.046

.046.046

.047.046

.041.029

.019.016

.015.009

.009.014

.019.029

.052.068

.060.066

.061.057

.050

5.048

.045.045

.045.046

.047.045

.035.019

.022.018

.020.018

.012.015

.028.038

.053.062

.060.068

.062.055

.049

6.051

.047.047

.047.046

.047.045

.034.019

.021.027

.027.024

.032.039

.042.045

.063.073

.069.073

.075.065

.054

7.054

.049.048

.047.047

.047.045

.037.028

.035.037

.039.041

.044.047

.050.053

.077.091

.085.088

.078.070

.055

8.057

.050.049

.049.048

.049.049

.043.037

.036.036

.040.042

.044.048

.059.073

.111.117

.105.102

.085.073

.059

9.054

.049.049

.048.047

.048.048

.045.033

.021.028

.034.037

.044.049

.065.077

.145.231

.164.107

.081.067

.056

10.052

.048.048

.048.048

.049.050

.047.040

.036.030

.036.040

.042.042

.046.056

.088.089

.093.070

.062.058

.051

11.050

.049.048

.048.048

.049.051

.048.044

.040.037

.035.032

.036.041

.051.073

.108.077

.072.063

.061.056

.051

12.051

.050.048

.049.049

.049.051

.049.045

.040.042

.042.041

.042.043

.046.067

.116.075

.076.067

.064.061

.053

$30 pe

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185165957

Attachment C: Ramping Proposal Heat Map Comparison

HE

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1.050

.049.048

.048.048

.049.050

.050.045

.039.040

.037.035

.032.033

.059.159

.159.074

.073.064

.059.056

.051

2.050

.048.048

.049.048

.049.051

.048.044

.037.041

.038.033

.036.041

.052.099

.177.093

.073.064

.058.058

.052

3.049

.046.047

.047.047

.047.048

.046.038

.021.014

.007.008

.007.012

.030.054

.127.150

.105.066

.060.059

.051

4.048

.046.046

.046.046

.047.046

.041.029

.019.016

.015.009

.009.014

.018.027

.085.122

.132.070

.061.057

.050

5.048

.045.045

.045.046

.047.045

.035.019

.022.018

.020.018

.012.015

.028.038

.086.084

.145.068

.062.055

.049

6.051

.047.047

.047.046

.047.045

.034.019

.021.027

.027.024

.032.039

.042.044

.095.076

.143.074

.075.065

.054

7.054

.049.048

.047.047

.047.045

.037.028

.035.037

.039.041

.044.047

.053.057

.103.084

.147.105

.079.070

.055

8.057

.050.049

.049.048

.049.049

.043.037

.036.036

.040.042

.044.048

.055.067

.138.144

.193.118

.086.073

.059

9.054

.049.049

.048.047

.048.048

.045.033

.021.028

.034.037

.044.049

.066.102

.198.625

.296.147

.083.067

.056

10.052

.048.048

.048.048

.049.050

.047.040

.036.030

.036.040

.042.042

.046.082

.165.120

.097.070

.062.058

.051

11.050

.049.048

.048.048

.049.051

.048.044

.040.037

.035.032

.036.041

.084.171

.164.077

.072.063

.061.056

.051

12.051

.050.048

.049.049

.049.051

.049.045

.040.042

.042.041

.042.043

.078.162

.161.075

.076.067

.064.061

.053

OR

A R

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1.050

.049.048

.048.048

.049.054

.058.045

.039.040

.037.035

.032.033

.043.086

.161.088

.073.064

.059.056

.051

2.050

.048.048

.049.048

.049.051

.048.044

.037.041

.038.033

.036.041

.046.063

.111.146

.073.064

.058.058

.052

3.049

.046.047

.047.047

.047.048

.046.038

.021.014

.007.008

.007.012

.030.058

.120.125

.086.066

.060.059

.051

4.048

.046.046

.046.046

.047.046

.041.029

.019.016

.015.009

.009.014

.023.038

.084.143

.060.066

.061.057

.050

5.048

.045.045

.045.046

.047.045

.035.019

.022.018

.020.018

.012.015

.028.041

.086.127

.069.068

.062.055

.049

6.051

.047.047

.047.046

.047.045

.034.019

.021.027

.027.024

.032.039

.042.048

.094.135

.084.074

.075.065

.054

7.054

.049.048

.047.047

.047.045

.037.028

.035.037

.039.041

.044.047

.050.054

.112.172

.092.105

.079.070

.055

8.057

.050.049

.049.048

.049.049

.043.037

.036.036

.040.042

.044.048

.073.114

.141.150

.143.118

.086.073

.059

9.054

.049.049

.048.047

.048.048

.045.033

.021.028

.034.037

.044.049

.066.078

.195.683

.286.147

.083.067

.056

10.052

.048.048

.048.048

.049.050

.047.040

.036.030

.036.040

.042.042

.046.079

.159.129

.093.070

.062.058

.051

11.050

.049.048

.048.048

.049.051

.048.044

.040.037

.035.032

.036.041

.067.139

.173.077

.072.063

.061.056

.051

12.051

.050.048

.049.049

.049.051

.049.045

.040.042

.042.041

.042.043

.046.111

.219.075

.076.067

.064.061

.053

Month

SC

E R

am

pin

g P

rop

osa

l

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Attachment D: Regression Results

SUMMARY OUTPUT 4 to 9 on‐peak

Regression Statistics

Multiple R 0.628391

R Square 0.394875

Adjusted R Square 0.39446

Standard Error 0.217014

Observations 8760

ANOVA

df SS MS F Significance F

Regression 6 268.9954646 44.83258 951.962512 0

Residual 8753 412.2216425 0.047095

Total 8759 681.2171071

Coefficients Standard Error t Stat P‐value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 0.041414 0.004921968 8.414198 4.57959E‐17 0.031766202 0.051063 0.031766202 0.051062631

SUMMER ON 0.150646 0.011868444 12.693 1.35732E‐36 0.12738119 0.173911 0.12738119 0.173911071

SUMMER OFF 0.074737 0.016503495 4.528537 6.01792E‐06 0.04238596 0.107087 0.04238596 0.107087417

SUMMER MID 0.014313 0.00667404 2.144624 0.032010371 0.001230619 0.027396 0.001230619 0.027395993

WINTER ON 0.065433 0.007936435 8.244595 1.89698E‐16 0.049875415 0.08099 0.049875415 0.080989972

WINTER SUPER OFF 0.011465 0.006468733 1.772406 0.076361939 ‐0.001215015 0.024145 ‐0.001215015 0.024145461

TOP 20 HOURS 3.479163 0.049516655 70.26249 0 3.38209911 3.576228 3.38209911 3.576227674

SUMMARY OUTPUT 3 to 8 on‐peak

Regression Statistics

Multiple R 0.686819

R Square 0.471721

Adjusted R Square 0.471359

Standard Error 0.177858

Observations 8760

ANOVA

df SS MS F Significance F

Regression 6 247.2449023 41.20748 1302.648845 0

Residual 8753 276.8889761 0.031634

Total 8759 524.1338784

Coefficients Standard Error t Stat P‐value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 0.040136 0.004312432 9.307067 1.63388E‐20 0.031682711 0.048589 0.031682711 0.04858947

SUMMER ON 0.124824 0.009732559 12.82536 2.56006E‐37 0.105745473 0.143902 0.105745473 0.14390168

SUMMER OFF 0.048583 0.013942377 3.484536 0.000495404 0.021252379 0.075913 0.021252379 0.07591305

SUMMER MID 0.014731 0.005678488 2.594172 0.009497697 0.003599805 0.025862 0.003599805 0.025862147

WINTER ON 0.059964 0.00668079 8.97564 3.40246E‐19 0.046868451 0.07306 0.046868451 0.073060289

WINTER SUPER OFF 0.008306 0.005426357 1.530614 0.125900941 ‐0.002331277 0.018943 ‐0.002331277 0.018942595

TOP 20 HOURS 3.376225 0.040463531 83.4387 0 3.296906531 3.455543 3.296906531 3.455542592

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Attachment E: SCE Response to ORA Data Request 5 “Regression Validation” Question 01: In response to ORA DR #1 (submitted in the email below), please submit workpapers in support of SCE’s performance measures of its TOU proposal. This is covered in testimony pages 69 through 72. Let me know if you have questions. Response to Question 01: Attached are two files: - the Excel file named 'Workpapers' includes all work related to pages 69 through 72 of the testimony; Tab 'Readme' explains the organization of the file - the 'Regression Validation' Word document provides details of the regression analysis that was performed. (included below)

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Regression Validation

The validation of the various TOU scenarios is established through a regression analysis where all the hours in a year are qualified by a series of binary variables defining their season and TOU period. These binary variables are then regressed on the hourly costs that have been established. The various proposed scenarios of season and periods are run through the analysis and the optimality is settled by the best explanatory power obtained in the regressions. Different scenarios are tested against the benchmark of SCE’s current structure for non-residential periods. Not only are proposed scenarios tested but also existing definitions such as the current residential period; the current rate 3 in the opt-in TOU residential pilot and the structure proposed by CAISO. The following graph shows what the regression does: it measures the fit of the straight horizontal lines against the cost curves in red and green for the months of October and July respectively. The width of the straight lines are set by the period definitions and the heights of the lines are given by the estimated coefficients, in effect a measure of the average costs in the respective periods.

  Since the top 20 hours are so extremely different from the other hours, they are put in their own group through their own binary variable. Once this is done, the sum of squared errors and the adjusted R square, measures of fit, increase considerably. The following tables present the results for the proposed periods with an on-peak period of 4 PM – 9 PM on summer (June through September) weekdays. Summer mid-peak is the same interval but on weekends. The same interval in winter (January Through May and October through December) is designated as winter mid-peak. There is a super off-peak period in winter for 8 AM – 4 PM. All other periods are off-peak.

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The omitted binary variable is the winter off-peak and thus, the estimated parameter of the intercept can be interpreted as the average cost of that period ($.05). The estimated parameters for the other periods should indicate the incremental variation of the average cost of the respective period over the winter off-peak. Thus, the summer on-peak average cost is about $.14 over the winter off-peak. The top 20 hours average over $3.5 incremental cost. It should be noted that the estimated parameters for the summer off-peak and winter super off-peak periods are not statistically significant; the interpretation of which is that the average costs of those periods do not differ markedly from the winter off-peak period. Regressions were run through SAS, but they could be run in Excel also as is shown in the ‘Workpapers’ file where the same scenario is put through the Excel regression analysis. The results are slightly different, probably due to differences in algorithms but they are also quite consistent. The ranking of the periods can be derived from different criteria by setting the sum of squared errors of the regression on the current TOU structure to 100% and by comparing all the other regressions to that baseline, results of less than 100% indicate that the considered structures provide a better ‘fit’ to the cost data; or by comparing R-square or Adjusted R-square measures.

Sum of Mean

Squares Square

Model 6 268.9955 44.83258 951.96 <.0001

Error 8753 412.2216 0.04709

Corrected Total 8759 681.2171

Analysis of Variance

Source DF F Value Pr > F

Root MSE 0.21701 R-Square 0.3949

Dependent Mean 0.07456 Adj R-Sq 0.3945

Coeff Var 291.0479

Parameter Standard

Estimate Error

Intercept Intercept 1 0.05288 0.0042 12.6 <.0001

SUMMID 1 0.06327 0.0163 3.88 0.0001

SUMON 1 0.13918 0.01159 12.01 <.0001

WINSOP 1 -0.01147 0.00647 -1.77 0.0764

SUMOFF 1 0.00285 0.00616 0.46 0.6438

WINON 1 0.05397 0.00751 7.19 <.0001

TOP20 1 3.47916 0.04952 70.26 <.0001

Parameter Estimates

Variable Label DF t Value Pr > |t|

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The results show that, as expected, scenarios with more definition such as the ones with three seasons and more time periods (e.g. addition of mid-peak shoulders in summer) fare better in this validation on a single measure. Also, scenarios where the peak period is narrower tend to do better as that narrowness delivers a better fit to the highly peaky cost distribution. However, SCE’s proposed scenario with an on-peak period of 4-9, while not the best in this validation process, performs better than the current residential TOUs and the CAISO’s proposal. The results also show that there are several scenarios that yield quite comparable results and therefore, customer considerations may rightfully be a deciding factor.