EXHIBIT 28 

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IN THE UNITED STATES DISTRICT COURT

DISTRICT OF PUERTO RICO

In re:

THE FINANCIAL OVERSIGHT AND 

MANAGEMENT BOARD FOR PUERTO 

RICO,

as representative of

THE COMMONWEALTH OF PUERTO RICO, 

et al.

Debtors.

PROMESA TITLE III

Case No. 17-BK-3283-LTS

(Jointly Administered)

In re:

THE FINANCIAL OVERSIGHT AND 

MANAGEMENT BOARD FOR PUERTO 

RICO,

as representative of

THE PUERTO RICO ELECTRIC POWER 

AUTHORITY,

Debtor.

Case No. 17-BK-4780-LTS

This Court Filing Relates Only to 

PREPA and Shall be Filed Only in Case 

No. 17-BK-4780-LTS and Main Docket 

17-BK-3283-LTS

REBUTTAL EXPERT REPORT OF SUSAN TIERNEY, PHD

MAY 15, 2023

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TABLE OF CONTENTS

I. Qualifications..........................................................................................................................1

II. Assignment .............................................................................................................................2

III. Summary of Opinions.............................................................................................................2

IV. Dr. George’s Analysis Confirms That FOMB’s Estimates Of Electricity 

Price Elasticity Are Substantially Overstated and Unreasonable ...........................................4

V. Dr. George Is Incorrect In Stating That PREPA Has Historically 

Overestimated Customer Loads............................................................................................12

VI. Dr. George’s And The FOMB’s Assessment of Capital Expenditures Is 

Inappropriate And Misses Key Variables.............................................................................17

VII. Dr. George Incorrectly Uses The “Just and Reasonable” Standard In His 

Analysis Of The Legacy Charge...........................................................................................24

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I. QUALIFICATIONS

1. I am Susan Tierney. I am a Senior Advisor at Analysis Group, Inc., an economics 

consulting firm. I have prepared and submit this Rebuttal Expert Report pursuant to Analysis 

Group’s retention by the Ad Hoc Group of PREPA Bondholders,1 Syncora Guarantee, Inc., 

Assured Guaranty Corp., and Assured Guaranty Municipal Corp. (collectively, “Bondholders”)

in connection with their objections to confirmation of a plan of adjustment (the “Plan of 

Adjustment” or “Plan”) filed by the Financial Oversight and Management Board (“FOMB” or 

“Oversight Board”) on behalf of the Puerto Rico Electric Power Authority (“PREPA”).

2. I previously submitted an opening expert report in this matter on April 28, 2023 

(“Tierney Confirmation Report”),2 in which, among other topics, I assessed certain inputs that 

FOMB used to support its Legacy Charge model, including: (i) the reasonableness of PREPA’s 

forecast of its customers’ demand for electricity (or “load forecast”); (ii) the reasonableness of 

FOMB’s assertions regarding the future price elasticity of demand for electricity; and (iii) the

reasonableness of FOMB’s projection of PREPA’s future capital costs.

3 My opening report 

discusses my qualifications and compensation in this matter. It also includes a copy of my 

curriculum vitae. 

1 The Ad Hoc Group of PREPA Bondholders includes BlackRock Financial Management, Inc., Franklin Advisers, 

Inc., GoldenTree Asset Management LP, Invesco Advisers, Inc., Nuveen Asset Management, LLC, Taconic Capital 

Advisors L.P., and Whitebox Advisors LLC.

2 Expert Report of Susan Tierney, PhD, April 28, 2023 (hereafter “Tierney Confirmation Report”).

3 Tierney Confirmation Report, ¶ 8.

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3. In preparing this expert report, I, along with Analysis Group staff working under 

my direction, have reviewed various documents and data sources. I attach a list of documents 

and sources I have considered as Appendix A to this expert report.

II. ASSIGNMENT

4. I have been asked to review the April 28, 2023 expert report of Dr. Glenn R. 

George (“George Report”) and assess his conclusions about the assumptions and inputs that the 

FOMB and its advisors at the Brattle Group use to derive the Legacy Charge—with my focus 

being on issues addressed in my Tierney Confirmation Report, including price elasticity, load 

forecast, and capital expenditures. I also was asked to address Dr. George’s application of the 

“just and reasonable” ratemaking standard and to review deposition testimony by Mr. William

Zarakas of the Brattle Group concerning certain assumptions in the Oversight Board’s Legacy 

Charge model. I do not discuss in detail every point in the George Report with which I disagree

(e.g., points that were already discussed in the Tierney Confirmation Report).

III. SUMMARY OF OPINIONS

5. Several of the conclusions contained in the George Report are erroneous, 

unsupported and in some cases directly contradicted by the data and analysis contained in that

report.

6. First, Dr. George’s analysis of the academic literature on electricity price 

elasticity actually confirms that, as I conclude in my opening report, the FOMB substantially 

overstates the extent to which future volumetric rate increases would suppress demand. The 

FOMB has adopted long- and short-run price elasticity estimates for residential customers that 

are far more negative than the overwhelming consensus of the studies that Dr. George examines. 

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One of the papers the FOMB uses as the basis for its long-run elasticity estimate is an 

unpublished working paper that has outlier elasticity results, employs an unusual methodology, 

focuses on a geographic area quite different from Puerto Rico, and comes to the opposite 

conclusion from the FOMB regarding the existence of an incremental price-elasticity impact on 

solar adoption.

7. Second, the George Report erroneously states that PREPA has historically overestimated future net load. In fact, as I concluded in my opening report, an apples-to-apples 

comparison of the correct net load metrics shows that PREPA has consistently underestimated 

net load and forecasted large declines in demand for electricity that have not materialized.

8. Third, the George Report fails to justify the FOMB’s inclusion in its Legacy 

Charge derivation of billions of dollars in unexplained additional capital expenditures not 

included in the 2022 PREPA Fiscal Plan.4 Neither Dr. George nor the Brattle Group team 

independently confirmed the reasonableness of this expanded capital reserve, which has the 

effect of dramatically reducing Net Revenues available to pay creditors. Both Dr. George and the 

Brattle Group team were simply instructed by counsel to accept the reasonableness of untested 

projections prepared by another consultant, McKinsey. The McKinsey projections run counter to 

PREPA’s own forecasts of its capital needs. My review of the regression analysis underlying 

these capital expense projections revealed multiple flaws—it produces statistically insignificant 

results, is based on inappropriate geographic comparables, omits key variables, and uses 

obviously wrong input data. Finally, the FOMB ignores the availability of billions of dollars in 

post-hurricane federal funding that might offset the need for PREPA-funded capital spending.

4 PREPA, “2022 Certified Fiscal Plan for the Puerto Rico Electric Power Authority,” FOMB_PREPA 00000699 -

FOMB_PREPA 00000882 (hereafter “2022 PREPA Fiscal Plan”).

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9. Finally, the George Report concludes without basis that the total customer rates 

resulting from FOMB’s proposed Legacy Charge constitute “just and reasonable” rates. Leaving 

aside that the “just and reasonable” standard is used mainly in traditional ratemaking proceedings

based on very near-term forecasts when forecasts are used at all, the Legacy Charge on its face 

does not meet the standard: A “just and reasonable” rate covers all of a utility’s total revenue 

requirements, including paying debt used to build and operate the utility’s system—something 

the Legacy Charge falls far short of accomplishing.

IV. DR. GEORGE’S ANALYSIS CONFIRMS THAT FOMB’S ESTIMATES OF

ELECTRICITY PRICE ELASTICITY ARE SUBSTANTIALLY OVERSTATED 

AND UNREASONABLE

10. As explained in Section VII of my opening report, the FOMB substantially 

overstates the extent to which future volumetric rate increases would suppress PREPA’s 

customers’ demand—the so-called price elasticity effect—by using assumptions that are 

unreasonable, inconsistent with academic literature, and unsupported by PREPA’s own prior 

load forecasts. Dr. Chakraborty explains, in her opening and rebuttal reports, that the FOMB’s 

overstatement of the elasticity effect of rate increases in turn causes the FOMB to understate the 

amount of potential new revenue that is available to fund creditor recoveries.

5

11. I disagree with Dr. George that the FOMB’s price elasticity estimates “fall within 

a reasonable range.”6 In fact, as I show below, Dr. George’s report demonstrates that the FOMB 

5 See Tierney Confirmation Report, Section VII; Expert Report of Maureen M. Chakraborty, PhD, April 28, 2023 

(hereafter “Chakraborty Confirmation Report”), Section VI; Expert Rebuttal Report of Maureen M. Chakraborty, 

PhD, May 15, 2023 (hereafter “Chakraborty Confirmation Rebuttal Report”), Section III.E.

6 Expert Report of Glenn R. George, MBA, PE, PhD, April 28, 2023 (hereafter “George Report”), Section III.A.2.a.

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is using price elasticity estimates much higher than the reasonable range seen in academic 

literature.7

12. The FOMB estimates residential customers’ long-run elasticity to be -1.7—i.e., it 

assumes that residential electricity demand will fall 1.7 percent for each 1 percent increase in 

volumetric electricity rates. As described in my opening report, that long-run elasticity is derived 

from two academic papers, Burke and Abayasekara (2018),8 published in The Energy Journal, 

and Buchsbaum (2022),9 an unpublished working paper.10 As shown in Figure 1, the FOMB’s 

elasticity estimate is more negative (i.e., produces a more negative effect in suppressing 

electrical demand) than the findings in nine of the ten academic studies that Dr. George

reviewed.

11

7 George Report, Section III.A.2.a.

8 Burke, Paul J. and Ashani Abayasekara, “The Price Elasticity of Electricity Demand in the United States: A ThreeDimensional Analysis,” The Energy Journal, Vol. 39, 2018 (hereafter “Burke and Abayasekara (2018)”), 

FOMB_PREPA 00020208 - FOMB_PREPA 00020243, available at https://doi.org/10.5547/01956574.39.2.pbur.

9 Buchsbaum, Jesse, “Long-Run Price Elasticities and Mechanisms: Empirical Evidence from Residential Electricity 

Consumers,” Energy Institute Working Paper 331, 2022 (hereafter “Buchsbaum (2022)”), FOMB_PREPA 

00022518 - FOMB_PREPA 00022589, available at https://haas.berkeley.edu/wp-content/uploads/WP331.pdf.

10 Tierney Confirmation Report, Section VII.B.

11 George Report, Table 2.

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Figure 1: Long-Run Price Elasticity Estimates for Residential Customers Identified in 

the George Report Table 2

Source:

[1] George Report, Table 2.

13. As Figure 1 shows, the unpublished Buchsbaum (2022) working paper used as 

part of the FOMB’s long-term elasticity derivation presented an elasticity estimate much more 

negative than the overwhelming consensus in the literature Dr. George surveyed. He does not 

explain in his conclusion that it is “reasonable” (and potentially even “conservative”)12 for the 

FOMB to have skewed its long-run price elasticity estimates based on one outlier result, rather 

than adopting an estimate within the lower range of similar estimates found in all of the other 

relevant literature that Dr. George reviewed.

14. Dr. George evidently adopts the decision made by the FOMB and the Brattle 

Group team to focus on just two of these sources: the Buchsbaum (2022) working paper and the 

Burke and Abayasekara (2018) study. Dr. George asserts that these two papers “are particularly 

12 George Report, ¶ 52.

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relevant because they are more recent and cover geographic regions similar to PREPA.”13 But, as 

explained in my opening report, neither of these studies supports the elasticity estimates for 

PREPA customers that are used in the FOMB’s Legacy Charge Derivation.

14

15. First, Buchsbaum (2022) does not study a “geographic region similar to PREPA”; 

that study focuses entirely on Pacific Gas & Electric (“PG&E”) customers in central and 

northern California. The PG&E service territory includes diverse areas with climates classified 

as Mediterranean, arid steppe, and cool highland,

15 while the PREPA service territory has a 

tropical climate.16 Buchsbaum emphasized that his elasticity findings were “specific to the 

geography and climate in this northern and central California sample.”17 Dr. George does not 

explain why he believes that northern and central California’s geography and climate mirror

Puerto Rico’s; clearly they do not.

16. Nor does Dr. George address the fact that the Buchsbaum (2022) price elasticity 

estimate is an outlier value that appears in an unpublished working paper employing unusual 

statistical methodologies—in Buchsbaum’s own words, “leverag[ing] a novel source of crosssectional price variation.”18

13 George Report, ¶ 48.

14 Tierney Confirmation Report, ¶¶ 87-92.

15 PG&E, “Electric Service Area Maps,” November 17, 2014, available at 

https://www.pge.com/tariffs/assets/pdf/tariffbook/ELEC_MAPS_Service%20Area%20Map.pdf; California 

Department of Fish and Game, “Climate and Topography,” Atlas of the Biodiversity of California, 2nd ed., 2021, 

available at https://wildlife.ca.gov/Data/Atlas.

16 World Bank, “Puerto Rico -Summary | Climate Change Knowledge Portal,” available at

https://climateknowledgeportal.worldbank.org/country/puerto-rico.

17 Buchsbaum (2022), pp. 4-5.

18 Buchsbaum (2022), p. 43 (emphasis added).

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17. Dr. George further notes that “Buchsbaum finds that, in the LR [long-run], lowincome customers are in fact more responsive to changes in price than are high income 

customers,”19 But Dr. George does not address that this finding is inconsistent with conclusions 

in multiple studies Dr. George also reviewed, each of which found that higher-income customers, 

who have more alternatives available to them, have more elastic demand.20

18. Dr. George fails to support or explain the FOMB’s derivation of a substantial

“Incremental Solar PV Effect” on residential elasticity of demand, which FOMB and the Brattle 

Group team says it relied upon Buchsbaum (2022) to justify and produce.

21 The FOMB asserts 

that -0.7 of its overall -1.7 elasticity estimate for residential customers results from that 

“Incremental Solar PV Effect.”22 As I observe in my opening report, however, Buchsbaum 

himself stated that his results “effectively rule out solar and energy efficiency programs as 

mechanisms driving the observed long-run elasticities.”23

19. I have since reviewed Mr. Zarakas’s testimony, in which he explained that his 

Brattle Group team’s introduction of an “Incremental Solar PV Effect” in the Legacy Charge 

model was actually meant to reflect any increased elasticity from all sources (not just from solar 

19 George Report, ¶ 48.

20 Yanming Sun and Yihua Yu, “Revisiting the Residential Electricity Demand in the United States: A Dynamic 

Partial Adjustment Modelling Approach,” The Social Science Journal, Vol. 54(3), 2017, pp. 295-304, available at 

https://doi.org/10.1016/j.soscij.2017.02.004, p. 302 (“[S]tates of relatively higher income levels are more price 

elastic than states of relatively lower income levels.”); Schulte, Isabella and Peter Heindl, “Price and Income 

Elasticities of Residential Energy Demand in Germany,” Energy Policy, Vol. 102, 2017, available at 

http://dx.doi.org/10.1016/j.enpol.2016.12.055, p. 512 (“[T]he behavioural response to energy price changes is 

weaker (stronger) for low-income (top-income) households.”); Deryugina, Tatyana, Alexander MacKay, and Julian 

Reif, “The Long(er) Elasticity of Demand: Evidence from Municipal Electric Aggregation,” Working Paper, 2017, 

available at https://www.haas.berkeley.edu/wp-content/uploads/T.-Deryugina.pdf, Figure 10.

21 “LT Elasticity workbook.xlsx,” FOMB_PREPA 00022590. 

22 “LT Elasticity workbook.xlsx,” FOMB_PREPA 00022590.

23 Buchsbaum (2022), p. 36 (emphasis added).

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PV panels)

24 between a “baseline” time period studied by Burke and Abayasekara (2018) (from 

2003 to 2015) and an updated time period studied in Buchsbaum (2022) (from 2008 to 2020).25 

Mr. Zarakas acknowledged that the time periods of the two studies substantially overlap and that 

both studies considered periods during which solar PV panels were available substitutes for 

price-sensitive consumers.26 Neither Mr. Zarakas nor Dr. George provide any basis on which to 

conclude that there was a 70% increase in residential price elasticity (from -1.0 to -1.7)27

between the time periods studied in those papers, or that a substantial shift in consumer behavior 

(instead of a different studied geography and population group) is what explains Buchsbaum 

(2022)’s outlier result.

20. Dr. George similarly fails to support the reasonableness of the FOMB’s reliance 

on Burke and Abayasekara (2018)’s price-elasticity assumptions. That report actually found a 

long-run residential elasticity figure of -1.0, which the authors themselves acknowledged was

24 Deposition of William Zarakas, In re: The Financial Oversight and Management Board for Puerto Rico, as a 

representative of the Commonwealth of Puerto Rico, et al., Debtors, No. 17-BK-3283-LTS, and In re: The Financial 

Oversight and Management Board for Puerto Rico, as a representative of Puerto Rico Electric Power Authority, 

Debtor, No. 17-BK-4780-LTS, United States District Court for the District of Puerto Rico, San Juan, Puerto Rico, 

May 4, 2023 (hereafter “Zarakas Deposition”), pp. 172:21-173:14 (Q: And so is the incremental solar PV effect 

intended to show an additional elasticity effect relating to rooftop solar adoption that is determined by Buchsbaum, 

but not determined by Burke? A: In part, correct. I’d say it’s intended to reflect PV, as well as other substitutes. Q: 

What other substitutes? A: Could be batteries. It could be different applications used for efficiencies. Q: When it 

says “including PV” here, it doesn’t mention those other substitutes. That’s because it was Brattle shorthand? A: 

Yes.”)

25 Zarakas Deposition, p. 171:20-25 (“Q: [...] [W]hat is the difference between the incremental solar PV effect of 

negative 1.4 on the one hand and the Burke and Buchsbaum values we just discussed? A: The difference is just the 

timing. Both include long-term prices elasticities from a variety of sources, including PV. The Burke paper was 

based on data through roughly 2015. So that negative 1 was kind of pre-widespread adoption and availability of 

substitutes, although the sub-substitutes are present there. And the later study has wider – more recent data and 

more, which reflects more wider availability of substitutes.”)

26 Zarakas Deposition, p. 172:17-20 (“Q: Are you aware that the years of the Burke dataset and the years of the 

Buchsbaum dataset overlap? A: Yes. I'm aware of that.”); p. 161:2-7 (“Q: Do you have an understanding as to 

whether the study performed by Burke and his coauthor excluded any effects on price elasticity from the availability 

of rooftop solar panels? A: It did not exclude the impact of price of PV on elasticity.”)

27 Dr. George presents the long-term elasticity estimate of Burke and Abayasekara as -0.95, but the Brattle Group 

reports it as -1.0. “LT Elasticity workbook.xlsx,” FOMB_PREPA 00022590.

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already “reasonably high” in the range of academic findings on the subject, yet is still well below

the -1.7 elasticity estimate that the FOMB ultimately adopted.

28

21. I also disagree with Dr. George’s attempt to distinguish two “meta-analyses,” 

Labandeira et al. (2017)29 and Zhu et al. (2018),

30 that I addressed in my opening report. These 

two studies each analyzed about 200 elasticity estimates in other studies and found that the mean 

long-term elasticity estimates in those studies is a fraction of those used by FOMB.

31 In 

particular, Dr. George asserts that these meta-analyses “may be less relevant” because they are 

(marginally) older than some of the others, and because studies from other parts of the world 

“may have less suitable geographies” than Puerto Rico does for the adoption of distributed 

generation alternatives.32 But both points ignore the realities of Puerto Rico: As I noted in the 

Tierney Confirmation Report, the substantial up-front installed costs for rooftop solar systems

(which Dr. George acknowledges),

33 and other logistical and legal hurdles (such as the number 

of renters who lack ownership or control of their rooftops) are barriers to the widespread 

adoption of distributed solar generation in Puerto Rico.34 The studies in both meta-analyses 

cover a broad range of geographies, including regions that are more similar to Puerto Rico than

28 Burke and Abayasekara (2018), p. 124.

29 Labandeira, Xavier, José M. Labeaga, and Xiral López-Otero, “A Meta-Analysis on the Price Elasticity of Energy 

Demand,” Energy Policy, Vol. 102, March 2017 (hereafter “Labandeira et al. (2017)”), available at 

https://www.sciencedirect.com/science/article/abs/pii/S0301421517300022.

30 Zhu, Xing, Lanlan Li, Kaile Zhou, Xiaoling Zhang, and Shanlin Yang, “A Meta-Analysis on the Price Elasticity 

and Income Elasticity of Residential Electricity Demand,” Journal of Cleaner Production, Vol. 201, November 10, 

2018, available at https://www.sciencedirect.com/science/article/abs/pii/S0959652618323588.

31 Tierney Confirmation Report, Section VII.B.3.

32 George Report, ¶ 47.

33 Dr. George agrees that distributed solar costs would be challenging: “[E]ven though rooftop solar has become 

more affordable over time, I note that customers must have the credit or disposable income necessary to participate 

in distributed generation and energy efficiency measures, which some of PREPA’s customers cannot afford.” 

George Report, ¶ 47.

34 Tierney Confirmation Report, ¶ 57.

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the mainland U.S.35 These meta-analyses—which aggregate hundreds of studies and show 

average long-run elasticities in the middle of the range established by the other studies Dr. 

George considered—remain the most reliable reference point for projecting elasticity in Puerto 

Rico. Dr. George offers no reason to alter my conclusion that the long-run elasticity estimates 

relied upon by the FOMB are extremely and unreasonably high.

22. Dr. George’s report also confirms that FOMB’s estimated short-run elasticities

are similarly out of line with the academic consensus. As shown in Figure 2, only two out of the 

eight estimates listed by Dr. George (including, again, the Buchsbaum (2022) working paper)

were more negative than the -0.2 estimate used by FOMB.

36

35 For example, the Labandeira et al. (2017) meta-analysis includes studies of elasticity of demand for Barbados and 

Jamaica, two Caribbean island nations in the same geographic region as Puerto Rico. Labandeira et al. (2017), pp. 

561, 565, 566.

36 George Report, Table 2.

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Figure 2: Short-Run Elasticity Estimates for Residential Customers Identified in the

George Report Table 2

Source:

[1] George Report, Table 2.

23. In sum, Dr. George’s own review shows that the FOMB’s long-run and short-run 

price elasticity estimates are more negative than the weight of the academic literature would 

support. Rather than substantiating his conclusion that FOMB’s estimates are “reasonable,” Dr. 

George’s report serves only to confirm that the FOMB overestimates the extent to which demand 

will fall in response to future rate increases, and thus understates the amount of future revenue 

available to creditors.

V. DR. GEORGE IS INCORRECT IN STATING THAT PREPA HAS 

HISTORICALLY OVERESTIMATED CUSTOMER LOADS 

24. In my opening report, I showed that PREPA substantially understated future 

electricity demand in the “Base Case” load forecast that the FOMB uses in its Legacy Charge 

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Model.37 As explained in Dr. Chakraborty’s rebuttal report, an overly pessimistic or understated 

load forecast will lead to a lower estimate of the amount of revenues that PREPA is capable of 

collecting to pay its creditors.

38 

25. Dr. George, however, says that “the primary risk [he] considered was the situation 

where PREPA’s forecasts had overestimated load.”39 He asserts that PREPA’s load forecasts for 

fiscal years 2017-2022, in the FY 2017 Fiscal Plan40 and the 2019 Integrated Resource Plan 

(“IRP”)41, “on average [...] overestimated load by about 2%.”42 Dr. George misinterprets these

data in two fundamental ways: first, he misses the point that the 2019 IRP forecast was not used 

in the 2022 PREPA Fiscal Plan or Revenue Envelope and Legacy Charge model (and so his 

comments about the IRP are irrelevant here); and second, he inappropriately commingles 

different metrics when he compares gross load to net load. As I explain below, Dr. George’s 

conclusion is wrong given the facts. PREPA did not overestimate loads in its Fiscal Plans during 

this period. As explained in my opening report, PREPA’s Base Case net load forecast 

demonstrates a persistent downward bias, as evidenced by an unambiguous track record of net 

load underestimation.43 

37 Tierney Confirmation Report, Section V.

38 Chakraborty Confirmation Rebuttal Report, Section III.D

39 George Report, ¶ 100 (emphasis added).

40 “PREPA Fiscal Plan Financial Model (170428) DRAFT.xlsx,” FOMB_PREPA 00024665 (hereafter “2017

PREPA Fiscal Plan Model”), “Inputs” tab.

41 Siemens, “Puerto Rico Integrated Resource Plan 2018-2019,” FOMB_PREPA 00024202 - FOMB_PREPA 

00024531 (hereafter “2019 IRP”), Section 3-1.

42 George Report, ¶ 101 (emphasis added).

43 Tierney Confirmation Report, Section VI.

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26. As shown in Table 1, in his analysis, Dr. George compares (a) a data series which 

combines the 2017 Fiscal Plan’s forecast of PREPA’s net load (the same as sales) for 201744 and 

the 2019 IRP’s forecast of “Gross Generation after EE and Customer Owned Generation” for

2019 through 2022,

45 to (b) the actual historical gross load (not accounting for distributed 

generation) for fiscal years 2017 and 2019-2022 as reported in the 2022 PREPA Fiscal Plan.

46

There are two problems with this comparison: First, it does not focus on the relevant metric. The

2019 IRP data series analyzed by Dr. George is a different measure of demand than the net load 

(i.e., net of energy efficiency and distributed generation) forecast from the 2022 PREPA Fiscal 

Plan that the FOMB actually uses in its Revenue Envelope and Legacy Charge models to 

determine revenue requirements and potential Legacy Charge revenues going forward.

47 Second, 

the comparison is not even apples-to-apples on its own terms. Actual gross load is not the metric 

that the net load and gross generation forecasts Dr. George cites are trying to forecast, so is not 

the right metric to compare with those forecasts. Put simply, the 2019 IRP generation forecast 

and historical gross load that Dr. George cites are irrelevant to any analysis of revenue 

requirements or the Legacy Charge. Therefore, Dr. George’s comparison is wrong and irrelevant 

to the “load forecast risk” that he discusses in his report.48

44 2017 PREPA Fiscal Plan Model, “Inputs” tab.

45 “Gross Generation after EE and Customer Owned Generation” accounts for energy efficiency, customer owned 

generation, technical and non-technical losses, auxiliary generation load, and PREPA own-use load. 2019 IRP, 

Exhibit 3-26.

46 FY 2018 is excluded due to impacts from Hurricanes Maria and Irma. “Revenue Envelope and Legacy 

Charge_protected.xlsx,” FOMB_PREPA 00025462 (hereafter “Revenue Envelope and Legacy Charge Model”), 

“Load” tab.

47 2022 PREPA Fiscal Plan Model; Revenue Envelope and Legacy Charge Model.

48 George Report, ¶ 99.

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27. The more relevant comparison is between: (a) the net load forecast from the 2017 

Fiscal Plan for 2017 and net load forecast from the 2019 Fiscal Plan49 for 2019 through 2022; 

and (b) actual historical net load (accounting for distributed generation). Such a comparison is 

apples to apples—the estimated versus actual values for the Fiscal Plan net load metric actually 

used in the FOMB’s Revenue Envelope and Legacy Charge model.50 As shown in Table 1, 

replicating Dr. George’s analysis using these data series reverses his conclusion: The 2017 Fiscal 

Plan and 2019 IRP forecasts do not overestimate load by “about 2%”; rather, the correct, revised

comparison shows that PREPA actually underestimated net load by an average of -5.42% from 

FY2017 through FY2022, and shows a persistent negative bias in each year.

51

49 PREPA, “2019 FOMB - Fiscal Plan for PREPA, as Certified by FOMB on June 27 2019,” June 27, 2019

(hereafter “2019 PREPA Fiscal Plan"), available at https://aeepr.com/es-pr/Documents/Exhibit%201%20-

%202019%20Fiscal_Plan_for_PREPA_Certified_FOMB%20on_June_27_2019.pdf.

50 Tierney Confirmation Report, Section VI.

51 Fiscal year 2018 is excluded from the analysis since Hurricanes Maria and Irma hit Puerto Rico during that fiscal 

year and caused widespread electricity market disruptions. Dr. George likewise excludes fiscal year 2018 from his 

analysis.

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Table 1: Revised George Report Load Forecast Comparison 

Notes:

[1] George Report forecasts are from 2017 PREPA Fiscal Plan for net load in 2017 and the 2019 IRP, series “Gross 

Generation after EE and Customer Owned Generation” (Exhibit 3-26) for 2019-2022. 

[2] George Report Actual Gross Load is from the 2022 PREPA Fiscal Plan Model, “Load” tab, row 14.

[3] Revised Comparison forecasts are from 2017 PREPA Fiscal Plan for 2017 and the 2019 Fiscal Plan for 2019-

2022.

[4] Revised Comparison Actual Net Load is from the 2022 PREPA Fiscal Plan Model, “Load” tab, row 160. 

[5] FY 2018 is excluded from the analysis since Hurricanes Maria and Irma hit Puerto Rico during that fiscal year 

and caused widespread electricity market disruptions. Dr. George likewise excludes FY2018 from his analysis.

Sources:

[1] 2017 PREPA Fiscal Plan Model.

[2] 2019 IRP.

[3] 2019 PREPA Fiscal Plan.

[4] 2022 PREPA Fiscal Plan Model. 

28. When the correct net load forecasts from PREPA’s 2017 and 2019 Fiscal Plans

are extracted and compared to the correct historical net load values for fiscal years 2017-2022, it 

is clear that PREPA underestimated net load for fiscal years going forward. his pattern is 

George Report Comparison

2017 PREPA Fiscal Plan and 2019 IRP 

Forecasts (GWh)[1]

Actual Gross Load Not Adjusted for 

Distributed Generation (GWh)[2]

Forecast (GWh) Source Forecast (GWh) Source

2017 17,051 2017 PREPA Fiscal Plan 

(Net Load) 17,162 -0.6%

2019 18,196 16,275 11.8%

2020 17,410 16,256 7.1%

2021 16,876 16,594 1.7%

2022 16,028 17,356 -7.6%

Average 17,112 16,728 2.46%

Revised Comparison

2017 and 2019 PREPA Fiscal Plan

Net Load Forecasts[3]

Actual Net Load Adjusted for 

Distributed Generation (GWh)[4]

Forecast (GWh) Source Forecast (GWh) Source

2017 17,051 2017 PREPA Fiscal Plan 

(Net Load) 16,996 0.3%

2019 15,764 16,050 -1.8%

2020 15,832 16,004 -1.1%

2021 14,772 16,280 -9.3%

2022 13,972 16,492 -15.3%

Average 15,478 16,364 -5.42%

Fiscal 

Year

Fiscal 

Year

2019 IRP (Gross 

Generation after EE and 

Customer Owned 

Generation)

2019 PREPA Fiscal

Plan (Net Load)

% Difference 

(Forecast / Actual)

% Difference 

(Forecast / Actual)

2022 PREPA Fiscal Plan 

(Net Load)

2022 PREPA Fiscal Plan 

(Gross Load)

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consistent with the analysis I conducted and explained in the Tierney Confirmation Report,

Section VI, which shows that PREPA’s Fiscal Plan net load forecasts have persistently estimated

declines in loads that have not historically occurred.52 Indeed, PREPA itself acknowledges the 

same pattern of load underestimation in its 2022 Fiscal Plan.53

VI. DR. GEORGE’S AND THE FOMB’S ASSESSMENT OF CAPITAL 

EXPENDITURES IS INAPPROPRIATE AND MISSES KEY VARIABLES

29. As explained in my opening report, PREPA’s assumptions about its future capital 

costs are a key input into its revenue requirement and rate forecasts, because such costs affect the 

amount of revenue that will need to be collected from customers. In the context of this 

proceeding, as I have explained, PREPA’s unreasonably high projections of future capital costs 

put downward pressure on the amount of potential revenues available to fund creditor recoveries. 

30. Dr. George’s report fails to justify these unreasonably high projections. He 

provides no basis for the FOMB’s upward departure from its own certified 2022 PREPA Fiscal 

Plan capital cost forecasts. He admits that none of the experts seeking to justify the additional 

large forecast of capital costs did any independent work to confirm their reasonableness, but

were simply instructed by counsel to accept the results generated by another consultant. And he 

therefore fails to have noticed significant errors in the consultant’s underlying regression analysis 

that make it an unreliable basis for the FOMB’s inflated capital forecast. The George Report also 

errs in ignoring the availability of billions of dollars in federal funding that could offset any 

increased capital needs, and in failing to question the need for an automatic $50 million annual 

52 Tierney Confirmation Report, Section VI.

53 2022 PREPA Fiscal Plan, Exhibit 62.

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reserve to cover a portion of FEMA-related projects that PREPA has elsewhere assumed would 

be covered by other public funding. 

31. Dr. George states that an independent consultant (identified as McKinsey by Mr. 

Zarakas54) retained by the FOMB created a projection of PREPA’s future transmission and 

distribution (or “T&D”) capital expenditures (or “CapEx”) based on “comparable utility 

companies in the southeastern US,” and estimated that PREPA would have an “Expected T&D 

CapEx” target of $250 million per year starting in FY2023 (adjusted thereafter for inflation).55

The Brattle Group team was then instructed to adopt that projection in determining the Legacy 

Charge for the fiscal years after 2034. Like the Brattle Group team, Dr. George also was “not 

able to independently verify this annual CapEx estimate” but nonetheless adopted it as a given 

when analyzing whether the Revenue Envelope Model had appropriately accounted for capital 

expenditures (even though those amounts exceeded the expected spending PREPA forecast in its 

own Fiscal Plan).56 Without such an independent confirmation of that annual CapEx estimate, it 

is not clear what basis Dr. George had for concluding that PREPA will need to fund an additional 

$2.425 billion (in nominal terms) in capital expenses over the next three decades—over and 

above the capital expenses PREPA has currently forecast in its certified 2022 Fiscal Plan—

except that he was instructed to do so. I did take a look at the available underlying models and 

documentation and have come to the opposite conclusion.

32. As an initial matter, and as I described in the Tierney Confirmation Report, the 

2022 PREPA Fiscal Plan already includes PREPA-funded capital cost estimates in 2034 to 2051, 

54 Zarakas Deposition, p. 217:8-13 (“Q: And where do these numbers come from, if you know? A: The same board 

advisor. Q: Did all of the numbers in this worksheet come from McKinsey? A: Yes.”)

55 George Report, ¶ 73 and footnote 89.

56 George Report, footnote 89.

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which are between $124.5 to $91.7 million lower than the inflated “Expected T&D CapEx” in 

each year.57 The FOMB has not explained why its own forecast for capital expenditures exceeds 

what PREPA concluded in the 2022 PREPA Fiscal Plan was sufficient to keep PREPA a viable 

operating entity—or why these additional costs have become evident only at this point in time. 

And Dr. George likewise ignores this in adopting the capital expenditure estimate as a given.

33. That aside, I have reviewed the McKinsey regression model used by the FOMB 

that underlies the “Expected T&D CapEx” assumed in the Revenue Envelope and Legacy 

Charge Model. For a variety of reasons, I conclude this regression model is not an appropriate 

tool for forecasting expected capital expenditures for PREPA.58 

34. The regression model59 of “Expected T&D CapEx” created by McKinsey is based 

on 16 utilities.60 Contrary to Dr. George’s description that this is based on the “spending of 

comparable utilities in the southeastern US,”61 the model goes beyond the southeastern US and 

includes utilities as far away as Oklahoma and Texas, without any explanation of the criteria on 

which the “comparable utilities” were chosen. The regression model attempts to estimate each 

utility’s annual 2022 T&D capital expenditures based a regression of each utility’s actual 2010-

2019 average T&D CapEx on three explanatory variables: (1) 2019 Medium Voltage 

57 Tierney Confirmation Report, ¶ 113.

58 “20230512 Utility Benchmark PREPA CAPEX.xlsx” (hereafter “McKinsey CapEx Regression Model”), tabs 

“CAPEX Regression (1),” “CAPEX Output (1).” Access to this regression model was first provided to me by the 

Oversight Board’s counsel on May 12, 2023, weeks after bondholders’ counsel sought such access at my request. 

For that reason, I was not able to consider this regression model in connection with my opening report.

59 Regression analysis is a statistical tool for analyzing historical data and determining the extent to which various 

combinations of explanatory (or “independent”) variables predict values for the outcome (or “dependent”) variable, 

in this case annual T&D CapEx spending. See e.g., Carter Hill, R., W.E. Griffiths, and G. C. Lim, Principles of 

Econometrics, 4th ed., John Wiley & Sons, Inc., 2011, pp. 40, 42.

60 McKinsey CapEx Regression Model.

61 George Report, ¶ 73.

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Transmission Network Length, (2) 2019 Number of Electric Customers, and (3) 2019 Total 

Electricity Delivered, but does not explain why those variables were chosen. The model forecasts 

a “Predicted 2022 Annual T&D CapEx” of $254 million for Puerto Rico, which is apparently 

then rounded to the $250 million target adopted by the FOMB. Figure 3 shows McKinsey’s 

CapEx regression results for PREPA and the other US utilities studied. 

Figure 3: Results of McKinsey’s Expected T&D CapEx Regression

Note:

[1] The data point labeled by McKinsey as “Actual 2010-2019 Average T&D CapEx” for PREPA actually appears 

to be the average of PREPA-funded T&D CapEx for only 2017 and 2019-2021 from the 2021 PREPA Fiscal Plan, 

excluding all FEMA-funded T&D CapEx.

Source:

[1] McKinsey CapEx Regression Model.

[2] “FOMB - May 2021 FP Model_Data_Room_vF2.xlsx,” FOMB_PREPA 00020362.

35. McKinsey’s regression model is flawed and unreliable for several reasons. First, 

the regression results fail to meet basic statistical tests of validity. When conducting a regression 

analysis, researchers aim to identify and quantify relationships between variables. The regression 

coefficients represent the estimated effects of the explanatory variables on the outcome 

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variable.62 “Statistical significance” tests are used to assess whether these coefficients are 

significantly different from zero.63 Statistical significance provides evidence that the relationship 

observed in the regression analysis is not likely to be a result of random variation in the data.64 In 

this case, none of the estimated coefficients on McKinsey’s explanatory variables are statistically 

significant at standard levels accepted in statistical research.

65 This implies that the estimated 

relationships between the explanatory variables in McKinsey’s model and utility T&D CapEx 

could simply be random chance or statistical noise.

36. Second, utilities in the “southeastern US” (as defined by McKinsey) span a wide 

range of geographies, climates, and utility-system characteristics quite different from Puerto 

Rico’s. A model calibrated on utilities in these locations is not useful or appropriate for 

predicting T&D capital spending in Puerto Rico. For example, McKinsey included in the dataset 

utilities in Texas and Oklahoma, even though Oklahoma and Texas have population densities, 

weather, and climates that are very different than Puerto Rico’s.66 It is inappropriate to compare 

62 Wooldridge, Jeffrey M., “Introductory Econometrics: A Modern Approach,” Cengage Learning International, 5th 

ed., 2012, pp. 22-24.

63 Specifically, hypothesis tests evaluate if the effect of a given coefficient is statistically different from zero at a 

given level of significance. If the test fails to refute this hypothesis, then we say that the effect of that variable is 

statistically insignificant at that level of confidence. Wooldridge, Jeffrey M., “Introductory Econometrics: A Modern 

Approach,” Cengage Learning International, 5th ed., 2012, pp. 128-129.

64 In most scientific work, the level of statistical significance required to reject the null hypothesis is set 

conventionally at 5%. Rubinfeld, Daniel L., “Reference Guide on Multiple Regression,” Reference Manual on 

Scientific Evidence, 3rd ed., National Academies, 2011, p. 431.

65 The coefficients from McKinsey’s regression model are not significant at standard 1%, 5%, or 10% levels.

Rubinfeld, Daniel L., “Reference Guide on Multiple Regression,” Reference Manual on Scientific Evidence, 3rd ed., 

National Academies, 2011, p. 431 (“In doing a statistical test, it is useful to compute an observed significance level, 

or p-value. The p-value associated with the null hypothesis that a regression coefficient is 0 is the probability that a 

coefficient of this magnitude or larger could have occurred by chance if the null hypothesis were true.”)

66 For example, the most recent 30-year historical climate normal data for the period of 1981-2010 indicated that 

Oklahoma City, Oklahoma observed 3,365 Normal Heating Degree Days and Houston, Texas observed 1,291, 

whereas San Juan, PR observed 0. For the same period, Oklahoma City, Oklahoma observed 2,099 Normal Cooling 

Degree Days and Houston, Texas observed 2,940, whereas San Juan, PR observed 5,855. Normal Heating Degree 

Days were measured between June and July of each year in the period and Normal Cooling Degree Days were 

measured between January and December of each year. Degree day data “estimates amounts of energy required to 

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PREPA-funded CapEx with the “Expected T&D CapEx” for dissimilarly situated utilities in 

other parts of the US.

37. Third, McKinsey’s regression model omits important variables relevant to T&D 

capital expenditures. A reasonable measure of expected capital expenditures for a utility for 

financial planning purposes would need to consider factors such as geography, climate, history 

of capital spending, the age and character of its infrastructure, and outside sources of capital 

funding.67 For example, a utility with a geographically concentrated customer base would have 

different capital spending requirements for its transmission system compared to a utility with 

customers that were highly spread out. McKinsey’s regression model includes none of these 

variables, and McKinsey does not explain how it chose the variables it used (or excluded).

38. Fourth, the source data used for McKinsey’s regression includes obvious factual 

errors. One of the explanatory variables used in the regression is 2019 Medium Voltage 

maintain comfortable indoor temperature levels” and “are computed from each day’s mean temperature (max + 

min/2)” such that any day’s mean temperature above or below 65 degrees Fahrenheit constitutes a heating or cooling 

degree day. Heating and cooling degree days are a straightforward way to compare climatic data observed at 

different major weather observing stations in mainland U.S., Puerto Rico, and the Pacific Island. See National 

Centers for Environmental Information, “Comparative Climatic Data (CCD),” available at 

https://www.ncei.noaa.gov/products/land-based-station/comparative-climatic-data.

67 For example, a report by the U.S. EIA discussed capital investments resulting from the replacement, 

modernization, and expansion of the distribution systems of major U.S. utilities. Age and character of the 

infrastructure would impact the amount an electric utility would spend on replacing current infrastructure. See U.S. 

Energy Information Administration, “Major utilities’ spending on the electric distribution system continues to 

increase,” available at https://www.eia.gov/todayinenergy/detail.php?id=48136. Additionally, climate, especially the 

frequency of extreme weather disasters such as hurricanes, impacts the amounts of outside sources of capital funding 

available to utilities. For example, federal funding provided by the U.S. Department of Housing and Urban 

Development (HUD) is available to privately-owned utilities impacted by major disasters through the Community 

Development Block Grant Disaster Recovery (CDBG-DR) funds (provided that HUD adopts alternative 

requirements or approves a waiver that permits allocation of CDGB-DR funds to privately owned utilities in 

connection with the disaster). U.S. Department of Housing and Urban Development, “Assistance to Privately 

Owned Utilities and Related Waivers,” available at https://files.hudexchange.info/resources/documents/CDBGMIT-Assistance-to-Privately-Owned-Utilities-and-Related-Waivers.pdf. See also, e.g., U.S. Department of Housing 

and Urban Development, “U.S. Community Development Block Grant Disaster Recovery (CDBG-DR) Private 

Utility Alternative Requirements Memorandum,” December 7, 2022, available at

https://www.hud.gov/sites/dfiles/CPD/documents/CDBG-DR/CDBG-DR-Private-Utility-Req-for-grants-PublicLaw-117-43_final.pdf.

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Transmission Network Length, which the McKinsey data set indicates having a value of zero for 

both Duke Energy Florida and Gulf Power. This is wrong. McKinsey does not include a 

definition of “Medium Voltage,” but lists PREPA’s Medium Voltage Transmission Network 

Length as 1,100 miles based on the 115 and 230 kilovolt (“kV”) transmission line mileage listed 

in the 2022 PREPA Fiscal Plan.68 According to Duke Energy’s annual report for 2019, Duke 

Energy Florida owned 2,529 miles of electric transmission lines with between 100 and 230 kV 

voltage, voltages in the same range as that of PREPA’s “Medium Voltage” transmission 

system.69 Gulf Power in 2019 owned approximately 9,500 miles of transmission and distribution 

lines in Florida, some of which would have been classified as “Medium Voltage” under any 

reasonable definition of the term.70 I have not confirmed all of McKinsey’s regression input data, 

but such obvious data errors call into question the validity of this regression model as a whole.

39. Separately from these severe flaws in the model itself, it is unreasonable for the 

FOMB and the Brattle Group team simply to adopt the results of McKinsey’s regression analysis 

as an amount of T&D capital expenditures that would be needed to be funded by PREPA 

ratepayers. By claiming that $2.425 billion (in nominal terms) of future additional revenue 

capacity needs to be reserved by PREPA for additional capital costs, the FOMB assumes that 

PREPA’s customer-provided revenues are the only source of money for its capital spending—a 

result that Dr. George does not question.71 This is not true. As I showed in the Tierney 

68 McKinsey CapEx Regression Model; 2022 PREPA Fiscal Plan, p. 27.

69 Duke Energy Corporation 2019 Form 10-K, filed February 20, 2020, p.35, available at 

https://s201.q4cdn.com/583395453/files/doc_financials/2019/ar/2019-duke-energy-form-10-k.pdf.

70 NextEra Energy, Inc. 2019 Annual Report, p. 95, available at 

https://www.investor.nexteraenergy.com/~/media/Files/N/NEE-IR/investor-materials/shareholderresources/2019%20NEE%20Annual%20Report.pdf.

71 George Report, ¶¶ 73-76.

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Confirmation Report, $14 billion in federal funding has been committed by the Federal Energy 

Management Agency (“FEMA”) and other federal agencies to help rebuild Puerto Rico’s 

damaged grid following Hurricanes Maria and Irma. $8.1 billion of this federal funding is 

committed for T&D “Rebuild and Transformation” spending on PREPA’s grid.72 This infusion 

of substantial federal funding is likely to mitigate the need for customer-sourced T&D capital 

expenditures in the decades to come. And Dr. George also fails to explain why it is reasonable 

for the FOMB to further assume a $50 million annual reserve from 2024 to 2033 for PREPA’s 

cost-share obligations on FEMA-related projects, given that PREPA itself and other Puerto Rico 

officials have stated that those costs would be covered by other public funding (i.e., Department 

of Housing and Urban Development grants).73

40. In conclusion, significant flaws make McKinsey’s regression model an 

inappropriate tool for forecasting expected capital expenditures in Puerto Rico. The FOMB is 

without any reasonable basis for its assumption (used in the Revenue Envelope Model) that 

PREPA will face incremental CapEx costs beyond those forecast by PREPA.74 And Dr. George 

is likewise without justification when he adopts those capital expenditure estimates as a given. 

VII. DR. GEORGE INCORRECTLY USES THE “JUST AND REASONABLE” 

STANDARD IN HIS ANALYSIS OF THE LEGACY CHARGE

41. Dr. George misleadingly argues that the Legacy Charge is consistent with the 

concept of “just and reasonable” rates.75 But even putting aside that this concept is rarely, if ever, 

72 2022 PREPA Fiscal Plan, p. 87.

73 See Tierney Confirmation Report, ¶¶ 110-111.

74 Tierney Confirmation Report, ¶ 114.

75 George Report, Section III.C.

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applied outside the context of a utility regulatory ratemaking proceeding, the Legacy Charge 

design would not be considered “just and reasonable” because it violates key utility ratemaking 

principles—including because it fails to provide recovery of the full cost of capital.

42. “Just and reasonable” is a standard legal and regulatory concept in utility 

ratemaking that generally requires regulators to balance the interests of multiple stakeholders 

(e.g., the utility and the providers of its capital, including creditors; its customers) when 

designing rates.

76 As noted in the utility regulatory literature, “[t]his balancing recognizes that 

while utility customers should pay rates that are reasonable, the rates must be sufficient to 

produce a profit level that enables the utility to maintain its financial integrity and attract 

capital.”77 As a former utility regulator, I am familiar with how the “just and reasonable”

standard is used in federal and state utility ratemaking.

78

43. Dr. George attempts to support his conclusion that the Legacy Charge is “just and 

reasonable” by pointing to the widely used Bonbright principles of utility ratemaking.

79 In 

76 Pechman, Carl, “Modernizing the Electric Distribution Utility to Support the Clean Energy Economy,” paper 

prepared for the U.S. Department of Energy, August 25, 2016, available at 

https://www.energy.gov/sites/prod/files/2017/01/f34/Modernizing%20the%20Electric%20Distribution%20Utility%

20to%20Support%20the%20Clean%20Energy%20Economy_0.pdf’; California Public Utilities Commission, 

“Utility General Rate Case – A Manual for Regulatory Analysts,” November 13, 2017, available at 

https://www.cpuc.ca.gov/-/media/cpucwebsite/files/uploadedfiles/cpuc_public_website/content/about_us/organization/divisions/policy_and_planning/ppd_

work/ppd_work_products_-2014_forward-/ppd-general-rate-case-manual-1-.pdf.

77 Van Nostrand, James M., “The Standard for Setting Utility Rates in Wyoming: Restoring the Required Balance 

between Investors and Customers,” Wyoming Law Review, Vol. 4, No. 1, 2004, p.1, available at 

https://scholarship.law.uwyo.edu/cgi/viewcontent.cgi?article=1078&context=wlr; Bluefield Water Works & 

Improvement Co. v. Public Service Commission of West Virginia et al., 262 U.S. 679 (1923), available at 

https://www.law.cornell.edu/supremecourt/text/262/679.

78 I note that there are some utilities that are publicly owned where the relevant legislative body allows the publicly 

accountable board of directors of such utilities to establish the utility’s rates without review and approval of public 

utility regulators. National Academies of Sciences, Engineering and Medicine, The Future of Electric Power in the 

U.S., The National Academies Press, 2021, available at https://doi.org/10.17226/25968. (As a member of the 

NASEM Committee on the Future of Electric Power, I am a co-author of this report.) 

79 George Report, ¶ 96.

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laymen’s terms, these principles include economic efficiency, financial sustainability for the 

utility, equity, fairness, and providing rates that are simple to understand and relatively stable 

over time.

80 But Dr. George applies these principles selectively and inappropriately. 

44. For starters, this proceeding is not a rate case, and so these principles are largely 

(if not entirely) inapplicable. The FOMB’s attempt to develop a Legacy Charge through a 

roughly 35-year forecast of both demand and costs is well outside of the norm of utility 

ratemaking. Utility rates are customarily set based on the utility’s “revenue requirement”—i.e.,

the revenues that will be necessary to cover the full cost of providing service to customers. When 

undertaking that analysis, the utility’s costs are known and measurable for a historical or nearterm future “rate year” using billing determinants (sales to customers) for the same rate year. I 

am not aware of any ratemaking case where rates were set based on a more-than-three-decade

forecast of costs or revenues. Dr. George does not address this distinction.

45. In any event—and most importantly—Dr. George is wrong to conclude the

Legacy Charge is “just and reasonable.” Under Dr. George’s own definition, one key aspect of 

“just and reasonable” rates is that they “allow the utility to be compensated for prudently 

incurred costs”81 and “effectively yield total revenue requirements under the fair return standard”

(which would include a fair return for equity investors, if any).

82 In this case, the debt service 

costs on legacy borrowings incurred by PREPA are part of its “total revenue requirement” that 

traditional ratemaking principles would not permit a utility simply to abandon—such debt was

80 Bonbright, James C., Principles of Public Utility Rates, Columbia University Press, 1961.

81 PREC, a predecessor to PREB, has acknowledged similar principles. Puerto Rico Energy Commission, Resolution 

in Response to PREPA’s May 25, 2017 Motion, In Re: Puerto Rico Electric Power Authority Rate Review, No. 

CEPR-AP-2015-0001, May 26, 2017 available at https://energia.pr.gov/wpcontent/uploads/sites/7/2017/05/Resolution-PREPAs-Motion-May-25-CEPR-AP-2015-0001.pdf.

82 George Report, ¶¶ 93, 96.

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used to fund PREPA’s past operations and capital improvements. If PREPA charges rates that 

are known to be insufficient to produce revenue to pay its legacy debt, then that necessarily

violates multiple Bonbright principles, including: (1) “revenue sufficiency” and (2) “efficiency” 

(i.e., charging customers a rate that signals the cost to provide them with utility service).83 

Accordingly, the Legacy Charge is not, contrary to Dr. George, “just and reasonable.”84

Signed on the 15th day of May, 2023.

__________________________________________

Susan Tierney

83 National Association of Regulatory Utility Commissioners, “Primer on rate design for cost-reflective tariffs,” 

January 2021, available at https://pubs.naruc.org/pub.cfm?id=7BFEF211-155D-0A36-31AA-F629ECB940DC.

Bonbright, James C., Principles of Public Utility Rates, Columbia University Press, 1961.

84 I acknowledge that, in a PROMESA proceeding, PREPA may be legally authorized to restructure its legacy debts 

(including, potentially, by reducing their amount). I am not offering any legal opinion on whether and in what 

circumstances such restructuring is permitted in a Title III proceeding. My point is simply that Dr. George 

selectively applies the “just and reasonable” ratemaking principle to a Legacy Charge that, by definition, violates 

that principle because it does not start—as traditional ratemaking does—from the premise that revenues must be 

generated from customers in total amounts sufficient to pay all prudent costs of the utility.

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APPENDIX A

Materials Considered

Legal Documents

Deposition of William Zarakas, In re: The Financial Oversight and Management Board for 

Puerto Rico, as a representative of the Commonwealth of Puerto Rico, et al., Debtors, 

No. 17-BK-3283-LTS, and In re: The Financial Oversight and Management Board for 

Puerto Rico, as a representative of Puerto Rico Electric Power Authority, Debtor, No. 

17-BK-4780-LTS, United States District Court for the District of Puerto Rico, San Juan, 

Puerto Rico, May 4, 2023. 

Expert Reports

Expert Report of Glenn R. George, MBA, PE, PhD, April 28, 2023. 

Expert Report of Maureen M. Chakraborty, PhD, April 28, 2023. 

Expert Report of Susan Tierney, PhD, April 28, 2023. 

Expert Rebuttal Report of Maureen M. Chakraborty, PhD, May 15, 2023. 

Bates Stamped Documents

Buchsbaum, Jesse, “Long-Run Price Elasticities and Mechanisms: Empirical Evidence from 

Residential Electricity Consumers,” Energy Institute, Working Paper 331, 2022, 

FOMB_PREPA 00022518 - FOMB_PREPA 00022589, available at 

https://haas.berkeley.edu/wp-content/uploads/WP331.pdf. 

Burke, Paul J. and Ashani Abayasekara, “The Price Elasticity of Electricity Demand in the 

United States: A Three-Dimensional Analysis,” The Energy Journal, Vol. 39, 2018, 

available at https://doi.org/10.5547/01956574.39.2.pbur. FOMB_PREPA 00020208 - 

FOMB_PREPA 00020243. 

“PREPA Fiscal Plan Financial Model (170428) DRAFT.xlsx,” FOMB_PREPA 00024665. 

“FOMB - May 2021 FP Model_Data_Room_vF2.xlsx,” FOMB_PREPA 00020362. 

“PREPA 2022 Fiscal Plan Model.xlsx,” FOMB_PREPA 00003018. 

“Revenue Envelope and Legacy Charge_protected.xlsx,” FOMB_PREPA 00025462. 

Siemens, “Puerto Rico Integrated Resource Plan 2018-2019,” FOMB_PREPA 00024202 - 

FOMB_PREPA 00024531. 

Data

“20230512 Utility Benchmark PREPA CAPEX.xlsx.” 

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Publicly Available Documents

Bonbright, James C., Principles of Public Utility Rates, Columbia University Press, 1961. 

California Department of Fish and Game, “Climate and Topography,” Atlas of the Biodiversity of 

California, 2nd ed., 2021, available at https://wildlife.ca.gov/Data/Atlas. 

California Public Utilities Commission, “Utility General Rate Case – A Manual for Regulatory 

Analysts,” November 13, 2017, available at https://www.cpuc.ca.gov/-/media/cpucwebsite/files/uploadedfiles/cpuc_public_website/content/about_us/organization/divisions/

policy_and_planning/ppd_work/ppd_work_products_-2014_forward-/ppd-general-ratecase-manual-1-.pdf. 

Carter Hill, R., W.E. Griffiths, and G. C. Lim, Principles of Econometrics, 4th ed., John Wiley & 

Sons, Inc., 2011 

Duke Energy Corporation 2019 Form 10-K, filed February 20, 2020, available at 

https://s201.q4cdn.com/583395453/files/doc_financials/2019/ar/2019-duke-energy-form10-k.pdf. 

Rubinfeld, Daniel L., “Reference Guide on Multiple Regression,” Reference Manual on 

Scientific Evidence, 3rd ed., National Academies, 2011. 

Labandeira, Xavier, José M. Labeaga, and Xiral López-Otero, “A Meta-Analysis on the Price 

Elasticity of Energy Demand,” Energy Policy, Vol. 102, March 2017, available at 

https://www.sciencedirect.com/science/article/abs/pii/S0301421517300022. 

National Academies of Sciences, Engineering and Medicine, The Future of Electric Power in the 

U.S., The National Academies Press, 2021, available at https://doi.org/10.17226/25968. 

National Association of Regulatory Utility Commissioners, “Primer on rate design for costreflective tariffs,” January 2021, available at 

https://pubs.naruc.org/pub.cfm?id=7BFEF211-155D-0A36-31AA-F629ECB940DC. 

National Centers for Environmental Information, “Comparative Climatic Data (CCD),” available 

at https://www.ncei.noaa.gov/products/land-based-station/comparative-climatic-data.

NextEra Energy, Inc. 2019 Annual Report, available at 

https://www.investor.nexteraenergy.com/~/media/Files/N/NEE-IR/investormaterials/shareholder-resources/2019%20NEE%20Annual%20Report.pdf. 

Pechman, Carl, “Modernizing the Electric Distribution Utility to Support the Clean Energy 

Economy,” paper prepared for the U.S. Department of Energy, August 25, 2016, 

https://www.energy.gov/sites/prod/files/2017/01/f34/Modernizing%20the%20Electric%2

0Distribution%20Utility%20to%20Support%20the%20Clean%20Energy%20Economy_0

.pdf. 

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PG&E, “Electric Service Area Maps,” November 17, 2014, available at 

https://www.pge.com/tariffs/assets/pdf/tariffbook/ELEC_MAPS_Service%20Area%20M

ap.pdf.

PREPA, “2019 FOMB - Fiscal Plan for PREPA, as Certified by FOMB on June 27 2019,” June 

27, 2019, available at https://aeepr.com/es-pr/Documents/Exhibit%201%20-

%202019%20Fiscal_Plan_for_PREPA_Certified_FOMB%20on_June_27_2019.pdf.

Puerto Rico Energy Commission, Resolution in Response to PREPA’s May 25, 2017 Motion, In 

Re: Puerto Rico Electric Power Authority Rate Review, No. CEPR-AP-2015-0001, May 

26, 2017 available at https://energia.pr.gov/wpcontent/uploads/sites/7/2017/05/Resolution-PREPAs-Motion-May-25-CEPR-AP-2015-

0001.pdf.https://energia.pr.gov/wp-content/uploads/sites/7/2017/05/Resolution-PREPAsMotion-May-25-CEPR-AP-2015-0001.pdf.

Schulte, Isabella and Peter Heindl, “Price and Income Elasticities of Residential Energy Demand 

in Germany,” Energy Policy, Vol. 102, 2017, available at 

http://dx.doi.org/10.1016/j.enpol.2016.12.055.

U.S. Department of Housing and Urban Development, “Assistance to Privately Owned Utilities 

and Related Waivers,” available at 

https://files.hudexchange.info/resources/documents/CDBG-MIT-Assistance-to-PrivatelyOwned-Utilities-and-Related-Waivers.pdf.

U.S. Department of Housing and Urban Development, “U.S. Community Development Block 

Grant Disaster Recovery (CDBG-DR) Private Utility Alternative Requirements 

Memorandum,” December 7, 2022, available at 

https://www.hud.gov/sites/dfiles/CPD/documents/CDBG-DR/CDBG-DR-Private-UtilityReq-for-grants-Public-Law-117-43_final.pdf.

U.S. Energy Information Administration, “Major utilities’ spending on the electric distribution 

system continues to increase,” available at 

https://www.eia.gov/todayinenergy/detail.php?id=48136.

Van Nostrand, James M., “The Standard for Setting Utility Rates in Wyoming: Restoring the 

Required Balance between Investors and Customers,” Wyoming Law Review, Vol. 4, No.

1, 2004, available at 

https://scholarship.law.uwyo.edu/cgi/viewcontent.cgi?article=1078&context=wlr.

Wooldridge, Jeffrey M., “Introductory Econometrics: A Modern Approach,” Cengage Learning 

International, 5th ed., 2012.

World Bank, “Puerto Rico - Summary | Climate Change Knowledge Portal,” available at 

https://climateknowledgeportal.worldbank.org/country/puerto-rico.

Yanming Sun and Yihua Yu, “Revisiting the Residential Electricity Demand in the United 

States: A Dynamic Partial Adjustment Modelling Approach,” The Social Science 

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Journal, Vol. 54(3), 2017, pp. 295-304, available at 

https://doi.org/10.1016/j.soscij.2017.02.004.

Zhu, Xing, Lanlan Li, Kaile Zhou, Xiaoling Zhang, and Shanlin Yang, “A Meta-Analysis on the 

Price Elasticity and Income Elasticity of Residential Electricity Demand,” Journal of 

Cleaner Production, Vol. 201, November 10, 2018, available at 

https://www.sciencedirect.com/science/article/abs/pii/S0959652618323588.

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