Economic Regulation of Electricity Distribution Utilities Under
High Penetration of Distributed Energy Resources: Applying an
Incentive Compatible Menu of Contracts, Reference Network
Model and Uncertainty Mechanisms
by
Jesse D. Jenkins
B.S. Computer and Information Science and Philosophy
University of Oregon, 2006
SUBMITTED TO THE ENGINEERING SYSTEMS DIVISION IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE DEGREE OF
ITUE
MASTER OF SCIENCE IN TECHNOLOGY AND POLICY
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
MAY 2 1 2014
JUNE 2014
LIBRARIES
LRAELOG
@ 2014 Massachusetts Institute of Technology. All rights reserved.
Signature of the Author:
Signature redacted
6/
Certified by:
Engineering Systems Division
May 15, 2014
Signature redacted
___
Ignacio Perez-Arriaga
Visiting Professor, Engineering Systems Division
Thesis Supervisor
Accepted by:
Signature red acted
Dava Newman
Professor of Aeronautids and Astronautics and Engineering Systems
Director, Technology and Policy Program
Economic Regulation of Electricity Distribution Utilities Under
High Penetration of Distributed Energy Resources: Applying an
Incentive Compatible Menu of Contracts, Reference Network
Model and Uncertainty Mechanisms
by
Jesse D. Jenkins
Submitted to the Engineering Systems Division
on May 15, 2014 in Partial Fulfillment of the
Requirements for the Degree of Master of Science in
Technology and Policy
ABSTRACT
Ongoing changes in the use and management of electricity distribution systems including the proliferation of distributed energy resources, smart grid technologies
(i.e., advanced power electronics and information and communication technologies)
and active system management techniques - present new challenges for the economic
regulation of electricity distribution utilities. In particular, regulators are likely to face
increased uncertainty regarding the evolution of network uses and the efficient cost of
network investments and maintenance, as well as an increased informational
disadvantage vis-a-vis the regulated utility. These challenges are especially important for
regulatory approaches that establish some share of the utility's allowed revenues ex
ante (e.g., incentive regulation, also known as revenue or price cap regulation, RPI-X,
performance-based regulation, or output-based regulation).
This thesis proposes a novel process for establishing the allowed revenues of an
electricity distribution utility and demonstrates its application as a practical solution to
the imminent regulatory challenges discussed above. The proposed method is a new
combination of three established regulatory tools: an engineering-based reference
network model (RNM) for forward-looking benchmarking of efficient network
expenditures; an incentive compatible menu of contracts to elicit accurate forecasts
from the utility and create incentives for cost saving efficiency efforts; and ex post
automatic adjustment mechanisms, or "delta factors," to accommodate uncertainty in
the evolution of network use and minimize forecast error.
2
Chapter 1 reviews the theoretical economic foundations of the regulation of network
monopolies, identifies emerging challenges in the regulation of electricity distribution
companies, and introduces the proposed regulatory method. Chapter 2 describes the
simulation of a realistic, large-scale urban distribution network used to demonstrate the
novel regulatory process proposed in this thesis. Chapter 3 uses the simulated distribution
network to demonstrate, step-by-step, the practical implementation of the novel
regulatory process, evaluates its performance, and summarizes the advantages for the
economic regulation of electricity distribution utilities under increasing penetration of
distributed energy resources.
Thesis Supervisor: Ignacio Perez-Arriaga
Title: Visiting Professor, Engineering Systems Division
3
Acknowledgements
This thesis builds on a rich intellectual foundation, without which this work would not
have been possible. I would like to acknowledge, in particular, the contributions of Rafael
Cossent and Tomas G6mez in developing a practical method for creating an incentive
compatible menu of contracts, Carlos Mateo, Tomas G6mez, Alvaro Sanchez-Miralles,
Jesus Peco, and Antonio Candela Martinez for their development of the reference
network model employed in this thesis, and to the regulatory staff at the UK Office of Gas
and Electricity Markets for their pioneering application of so many regulatory best
practices (and the numerous, transparent publications detailing their thinking and lessons
learned).
I would also like to thank my fellow research assistants on the Utility of the Future Project,
Ashwini Bharatkumar and Scott Burger, for helping me think through some of the many
road blocks encountered on the route to completing this thesis (or at least listening to me
explain them!).
Above all, I am grateful to two individuals: Ignacio Perez-Arriaga, whose course on the
regulation of the electric power sector is the best introduction to the field any student
could ask for and whose guidance and advice during this thesis were invaluable; and
Claudio Vergara, who developed the network simulation approach employed herein and
who generously offered hours of critical assistance as I extended his methods for use in
this thesis.
Finally, I would like to thank my wife, who supported me through so many long hours.
4
Contents
Chapter 1 - Economic Regulation of Electricity Distribution Utilities:
Introduction, Emerging Challenges, and New Solutions
6
1.1- An Introduction to Network Regulation
6
1.2 - Emerging Challenges in the Regulation of Electricity Distribution Utilities
15
1.3 - New Solutions for Regulation of Distribution Utilities Under High Penetrations
of Distributed Energy Resources
20
1.4 - Structure of the Thesis
29
References
30
Chapter 2 - Constructing a Simulated Electricity Distribution Network
35
2.1 - Specification of Simulation Parameters
35
2.2 - Assigning Load Profiles
37
2.3 - Determining Network Topology and Assigning Geographic Location of Loads
39
2.4 - Constructing the Network with the Reference Network Model
43
2.5 - Simulating Network Expansion Scenarios
47
References
52
Chapter 3 - Demonstrating the Proposed Regulatory Process
54
3.1 - Forecasting the Evolution of Network Uses
54
3.2 - Establishment of Regulator's Ex Ante Estimate of Efficient Expenditures
58
3.3 - Construction of an Incentive Compatible Menu of Contracts
65
3.4 - Calculation of Ex Ante TOTEX and Revenue Baselines and Sharing Factor
70
3.5 - Calculation of Automatic Adjustment Factors to Manage Uncertainty
74
3.6 - The Ex Ante Regulatory Process: Applying Annual Corrections
78
3.7 - Evaluating the Performance of the Regulatory Process
83
3.8 - Summary of Advantages of the Proposed Regulatory Process
88
References
90
5
Chapter 1: Economic -Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
1 - Economic Regulation of Electricity Distribution Utilities: Introduction,
Emerging Challenges, and New Solutions
1.1 - An Introduction to Network Regulation
Electricity distribution is a natural monopoly activity. Due to the existence of economies
of scale (e.g., average costs decline as output increases), marginal costs for distribution
networks are lower than average costs. It is therefore less costly for a single firm to
supply network services in a given territory rather than multiple firms (that is, firm costs
are subadditive). In addition, the capital-intensive nature of distribution networks and
the existence of considerable sunk costs in existing networks create significant barriers
to entry for new competitors. This combination of economies of scale and significant
barriers to entry make electric distribution activities a poor candidate for market
competition, thus requiring regulatory intervention to prevent monopoly abuse of
market power (Cossent, 2013; G6mez, 2013a, 2013b; Joskow, 2005).1
Distribution utilities have therefore been subject to various forms of economic
regulation since very early in the history of the electric power sector, either via public
ownership or oversight of private distribution companies by a regulatory body capable
of, at minimum, establishing allowed revenues and/or prices. Furthermore, distribution
networks (along with transmission network activities) remain a regulated sector even in
electricity markets that have undergone privatization and/or deregulation of generation
or retail activities. Indeed, these regulated network activities provide the infrastructure
platform for both the generation and retail market activities, whether liberalized or not.
Effective regulation of electricity network utilities is thus a critical cornerstone of any
well-functioning, competitive market segments and has important welfare
consequences for electricity consumers and society (Joskow, 2013).
Regulatory frameworks for network utilities in the electric power sector and elsewhere
have evolved over time based on both practical experience and advances in economic
and regulatory theory. In general, traditional cost-of-service regulation is gradually being
supplanted by incentive or performance based regulation, which is designed to create
stronger incentives for network utilities to reduce costs through efficiency gains.
Incentive regulation of electricity transmission and distribution utilities has become
1 Of course, the costs of imperfect markets must be balanced against the costs of imperfect regulation
(Joskow, 2010). It may also be possible to discipline natural monopolies by introducing competition for
the market (i.e., via regular franchise auctions) as opposed to competition in the market. However,
several considerations make electricity distribution a poor candidate for the introduction of franchise
competition, including: the long-lived assets and significant sunk costs associated with distribution
networks; considerably advantages afforded to incumbent operators versus other bidders due to
information asymmetries; and the currently evolving nature of distribution network technologies and
demands for system services (see Cossent (2013) for in-depth discussion of these issues).
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Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
widespread in Europe (Cossent, G6mez, & Frfas, 2009; Jamasb & Pollitt, 2007) and Latin
America (Rudnick, et al., 2007) following privatization, restructuring, and liberalization
of these markets beginning in the 1990s. In addition, while the United States is still
broadly characterized by a mix of municipal or other publicly-owned distribution utilities
and cost-of-service regulated private utilities, elements of performance or incentivebased regulation have begun to be adopted with increasing frequency (Joskow &
Schmalensee, 1986; Joskow, 2013).
Regardless of which method is employed, economic regulation of distribution utilities
must confront several general regulatory challenges, including incomplete and
asymmetric information, a firm participation constraint, and resulting opportunities for
strategic behavior on the part of regulated firms. In addition, regulators must carefully
balance fundamental tradeoffs between increasing allocative efficiency and avoiding
moral hazard on the one hand and increasing X-efficiency and minimizing adverse
selection on the other hand (as discussed further below).
The economic regulation of electricity distribution utilities involves two key tasks: first,
the regulator must determine the sum of revenues the regulated utility is allowed to
collect to remunerate their operating and investment costs (the cost recovery or
remuneration challenge); and second, the regulator must determine how the utility
should collect these revenues from their network users (the cost allocation or tariff
design challenge).
The cost allocation challenge lies outside the scope of this thesis and so will not be
discusses further herein. Moreover, while fair and efficient tariff design is an essential
regulatory undertaking, regulators must typically first determine the firm's total allowed
revenues, balancing allocative efficiency, X-efficiency, and the firm participation
constraint (Cossent, 2013). This determination of allowed revenues is at least as
important as tariff design, and the regulated remuneration of distribution utilities
establishes the principal financial incentives for the regulated firm. The practical
challenges associated with this key task have spawned considerable advancement in
both regulatory theory and practice.
The theoretical economic foundations of the cost recovery or remuneration challenge
lie in contract theory, particularly the design of regulatory contracts under imperfect
and asymmetric information (see Bolton & Dewatripont, 2005; Joskow, 2005, 2008,
2013; Laffont & Tirole, 1993).
The first challenge facing the regulator is that of imperfect information: the regulator
does not know the utility's cost or service quality opportunities ex ante, nor can the
regulator directly observe the utility's managerial effort to capture efficiency
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Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
opportunities (Joskow, 2013). As a result, the utility knows much more about their cost
opportunities than the regulator, introducing considerable information asymmetries. In
addition, the regulator must assess the prudency and efficiency of capital-intensive
utility investments with relatively long asset lives (often measured in decades). This
introduces additional challenges associated with uncertainty about future technological
change and demand for network services.
Together, these challenges create a significant opportunity for strategic behavior on the
part of the regulated utility, wherein the firm uses its information advantage in the
regulatory process to increase its allowed revenues and profits or achieve other
managerial objectives (Averch & Johnson, 1962; Jamasb, Nillesen, & Pollitt, 2003, 2004;
Joskow, 2013; Laffont & Tirole, 1993). In particular, the firm would like to convince the
regulator that it is a higher cost firm than it really is, taking advantage of the regulator's
need to comply with the firm participation constraint (e.g. ensure the financial viability
of the regulated firm).
To illustrate this challenge, consider a simple case where the utility is either of a "high
cost" type or a "low cost" type. 2As illustrated in Table 1-1, if the regulator believes the
firm is of a low cost type but it is in actuality a high cost type, the firm will not recover its
costs and the regulator will violate the firm participation constraint. In contrast, if the
regulator believes the firm is a high cost type, the firm participation constraint will be
met regardless of the firm's true cost type, although excess profits (or rents) will result
in the case that the firm is in actuality a low cost utility. The social welfare maximizing
regulator therefore faces an adverse selection problem as it seeks to adhere to the firm
participation constraint in the face of incomplete information about the firm's true cost
type and strategic behavior on the part of the regulated utility (Joskow, 2013).
Table 1-1: Adverse Selection and the Firm Participation Constraint
Regulator's Belief Regarding
Utility's Actual Cost Type
the Utility's Cost Type
Low
High
Low
Break-even
Fail to break-even
High
Excess profits
Break-even
This leads to a final regulatory challenge: the creation of moral hazard. The regulated
firm's actual cost type is dynamic, not fixed, and it depends in part on the exertion of
managerial effort to identify and exploit opportunities for cost reduction and/or
performance improvement. Yet the regulator cannot directly observe this managerial
2
The discussion that follows is drawn in large part from Cossent (2013), G6mez (2013b), and
Joskow (2013).
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Jesse D. Jenkins (2014)
effort either (another case of incomplete information). If the regulator establishes
allowed revenues that are too high or are set so as to ensure firm participation
regardless of the degree of actual managerial effort, the firm's management will have
no incentive to pursue cost savings. As Joskow (2013) colorfully puts it, "If the rat
doesn't smell the cheese and sometimes get a bit to eat, he may play golf rather than
working hard to achieve efficiencies for the regulated firm."
This discussion illustrates a broader tradeoff the regulator faces between incentivizing
managerial effort by creating financial incentives for the firm to pursue cost savings on
the one hand and minimizing economic rents collected by the utility from ratepayers on
the other hand (Joskow, 2013). In economic terms, this is a tradeoff between productive
efficiency or X-efficiency-that is, ensuring firms are optimizing their production
functions to minimizing the inputs used to produce a given level of output-and
allocative efficiency-that is, ensuring prices reflect costs and social welfare is
maximized by eliminating deadweight losses due to monopoly abuse of market power.
Figure 1-1: Fundamental Tradeoffs in Network Utility Regulation
Goals
increase allocative efficiency vs.
Minimize Rent Extraction
Increase X-efficiency
vs.
Incentivize cost savings
vs.
Adverse selection
Challenges
Moral hazard
Economic theory and regulatory practice have evolved over time to respond to and
manage these fundamental regulatory challenges. Two general approaches to the
regulation of network utilities have emerged, which, in their "pure" form, reflect
alternative approaches to the tradeoffs described above: cost-of-service regulation and
incentive regulation. While, in practice, regulatory mechanisms generally depart from
these pure forms, discussion of each archetype can help illustrate the core features of
these alternative regulatory approaches. We also note that in either case, regulators
establish allowed revenues with reference (implicitly or explicitly) to an established level
of service quality (typically enacted through minimum standards and/or incentives
regarding reliability and losses).
In its pure form, cost-of-service regulation (or rate-of-return regulation) is essentially a
"cost-plus" contract negotiated between the regulator (on behalf of ratepayers and
society) and the utility (G6mez, 2013b). The regulator sets allowed revenues for the
utility based on ex post evaluation of recently incurred utility costs (i.e., looking back at
9
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
the preceding year), known as a "historic test year," or based on relatively short-term ex
ante estimates of costs (i.e., looking ahead at the following year), known as a "future
test year." In either case, the regulator aligns revenues with realized costs plus a
regulated rate of return through frequent ex post reviews and adjustments. This
approach is designed in such a way as to ensure the regulated firm recovers all of the
costs incurred 3 while earning a fair rate of return on their investments. Since allowed
revenues are projected ex ante for only a short time period and frequently reviewed ex
post, this approach mitigates uncertainties about the evolution of distribution costs and
network demands. In addition, the assurance of a fair return on investments ensures the
firm participation constraint is met and necessary investment is attracted to the sector.
In addition, if the rate of return is set efficiently-that is, high enough to attract
sufficient investment into the sector and not too high so as to charge ratepayers more
than necessary-then the regulator can maximize allocative efficiency and avoid
transferring excess economic rents from ratepayers to the utility.
The tradeoff inherent to this pure cost-of-service approach is that regulated firms have
little-to-no incentive to pursue cost saving efficiency efforts. That is, the moral hazard
problem is entirely unmitigated and significant X-inefficiencies remain. In fact, if the
regulator establishes an allowed rate of return that exceeds a fair cost of capital-either
due to asymmetric information and adverse selection or deliberate strategic behavior
on the part of the utility-then the firm will be incentivized to substitute capital for
other inputs (e.g., labor) and over-invest in capital, further exacerbating X-inefficiencies.
This over-investment is known as the Averch-Johnson effect (Averch & Johnson, 1962).
The pure form of incentive regulation (also known as price cap, revenue cap, or
performance regulation) takes the opposite approach (Beesley & Littlechild, 1989;
Laffont & Tirole, 1993). The regulator caps allowed revenues or prices ex ante for a set
period (e.g., 3-8 years). Firm profitability and returns on investment thus depend on the
utility "beating the cap"-that is, reducing realized costs below the price or revenue
cap. This approach eliminates the moral hazard problem and creates a high-powered
incentive for the exertion of managerial effort to optimize X-efficiencies.
Again, there are clear tradeoffs to a pure incentive regulation approach. If the regulator
sets the price or revenue cap too low, the firm may be unable to attract necessary
investments or remain profitable. Given uncertainties about the evolution of technology
and demands for network services after the ex ante cap is established, this approach
may therefore risk violating the firm participation constraint. Alternatively, to avoid
violating this constraint, the regulator may have a strong incentive to set the cap too
In practice, regulators typically limit cost recovery or returns on investment only to "prudently incurred"
expenditures, as discussed further below.
3
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Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
high, incurring the full adverse selection problem and undermining allocative efficiency
by leaving potentially large rents to the utility (a transfer of welfare from ratepayers).
In summary, a pure cost-of-service regulatory approach trades off allocative efficiency,
minimization of rents, and assurance of firm participation for unmitigated moral hazard
and negligible incentives for X-efficiency. In contrast, a pure incentive regulation
approach establishes strong incentives for X-efficiency and eliminates moral hazard yet
risks either violating the firm participation constraint or succumbing to adverse selection
and leaving considerable rents to the regulated firm (reducing allocative efficiency).
Table 1-2: Summary of "Pure" Approaches to Network Regulation
Cost-of-Service Regulation (Rate-of-Return)
Incentive Regulation (Price/Revenue Cap)
*
Essentially "cost-plus" contract with firm
earning regulated rate of return
0
Caps prices or revenue ex ante for a
set period
*
Prices or revenues set based on realized
costs reviewed ex post
0
Firm profitability depends on
"beating the cap"
*
Pros: maximizes allocative efficiency and
minimizes economic rents extracted by
the firm; ensures firm participation
0
Pros: eliminates moral hazard and
creates strong incentives to maximize
X-efficiency
*
Cons: Negligible incentives for X-efficiency,
creating moral hazard problem
0
Cons: To avoid violating firm constraint,
may incur full cost of adverse selection
and leave rents to regulated firm
Faced with the tradeoffs and challenges inherent in the regulation of network utilities
under information asymmetries, the social welfare maximizing regulator will, in practice,
seek mechanisms that balance both the costs of adverse selection and moral hazard and
the benefits of allocative and X-efficiency, all while abiding by the firm participation
constraint. In addition, the regulator will seek mechanisms to reduce their informational
disadvantage as much as possible.
Indeed, while the pure forms of cost-of-service and incentive regulation lie at extreme
ends of the tradeoff space illustrated in Figure 1-1, the actual implementation of either
approach tends to lie somewhere in between.
Under cost-of-service-based approaches, regulators can instill some incentive for
managerial efficiency by subjecting investments to an ex-post review. That is, the
regulator will allow the utility a return only on those investments deemed to be
"prudently incurred" as part of periodic regulatory reviews or "rate cases." In addition,
as the regulator reviews the utility's investments only periodically rather than
continuously, there exists a delay between the time when the utility makes an
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Jesse D. Jenkins (2014)
investment or incurs a cost and the time when allowed revenues are adjusted
accordingly. This dynamic creates an additional incentive for the firm to reduce costs, as
prices or revenues are in practice fixed for some period of time (which may extend to
several years). In addition, cost-of-service approaches have frequently been blended
with various, targeted performance-based incentives for efficiency or quality (Joskow &
Schmalensee, 1986).
Similarly, real-world implementations of incentive regulation feature regular "ratchets"
of the cap-that is, after a fixed interval of time (i.e., 3-8 years), the regulator will review
the utility's actual costs and performance and reset the revenue or price cap based on
that review. These price ratchets effectively transfer the economic savings due to
exertion of managerial effort from the firm back to ratepayers. The ratchet also reduces
incentives for cost saving, as a dollar of cost reduction only entitles the firm to
additional revenues for early on in the regulatory period is worth more to the utility
than a dollar of savings implemented just prior to the next ratchet of the cap. 4 In reality
then, incentive regulation does not provide the maximum incentive for managerial
effort, nor does it wholly ignore allocative efficiency in its pursuit of optimal X-efficiency.
Indeed, depending on the frequency of ratchets under incentive regulation and the
length between rate cases under cost-of-service, real-world implementations of these
alternative regulatory approaches may fall quite close together on the spectrum
illustrated in Figure 1-1.
Given the inherent tradeoffs between regulatory approaches, it is perhaps not
surprising that theoretical developments indicate that the preferable regulatory
mechanism is a balance between a pure cost-of-service approach and a pure price or
revenue cap incentive approach. This approach takes the form of a sliding scale
regulatory mechanism in which allowed revenues are partially fixed ex ante so as to
create incentives for cost reduction and partially responsive ex post to changes in
realized costs to improve rent extraction and mitigate uncertainty (G6mez, 2013b;
Joskow, 2013; Schmalensee, 1989). This regulatory regime effectively shares profits and
rents as well as risks between the utility and ratepayers based on a profit-sharing factor
(or efficiency incentive rate) between one (corresponding to a pure price/revenue-cap
incentive regulation) and zero (corresponding to a pure cost-of-service approach). The
regulator can choose the precise sharing factor to manage tradeoffs between incentives
for efficiency and rent extraction and in response to the degree of uncertainty about
future costs and demand. In particular, under lower levels of uncertainty, a higher
profit-sharing factor (i.e., the firm is exposed to most of the risks and rewards of cost
4 Cost-saving incentives can be equalized throughout the regulatory period by implementing a rolling
efficiency incentive payment that allows a portion of efficiency savings to be earned in the following
regulatory period (as demonstrated in Chapter 3).
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Jesse D. Jenkins (2014)
savings) performs better, while a lower profit-sharing factor (which shares most risks
and rewards with ratepayers) performs better under higher levels of uncertainty
(Schmalensee, 1989).
Furthermore, the regulator can improve on a single profit-sharing factor by offering a
regulated utility a menu of regulatory contracts with a continuum of different sharing
factors (Cossent & G6mez, 2013; Laffont & Tirole, 1993). This menu of contracts allows
the firm to play a role in selecting the strength of incentives for cost saving. If
constructed correctly, this menu will establish "incentive compatibility"-that is, the
design of the menu ensures that a profit-maximizing firm will always be better off (i.e.,
earn the greatest profit and return on equity) when actual expenditures match their ex
ante estimate of necessary expenditures. Incentive compatibility thus eliminates
incentives for firms to artificially inflate their cost estimates while incentivizing firms to
reveal their true cost types to the regulator, helping minimize strategic behavior and
overcome information asymmetries. Furthermore, a profit-motivated firm with less
opportunity to reduce costs will choose a low-powered incentive, while a firm with large
efficiency opportunities will choose a high-powered incentive. Despite strong theoretical
advantages, the use of a menu of regulatory contracts in the regulation of electricity
distribution utilities has been very limited in practice (Cossent & G6mez, 2013; Cossent,
2013; Joskow, 2013; Ofgem, 2009, 2010b, 2013c). Chapter 3 will discuss the
construction and implementation of an incentive compatible menu of regulatory
contracts further.
In addition to tailoring cost-of-service and incentive regulation approaches to manage
these inherent tradeoffs, regulators have also developed various methods to overcome
information asymmetries.
Regulatory accounting systems are integral to both approaches. These systems involve
systematically collecting data from regulated firms regarding their costs, assets,
performance indicators, and other information needed in the regulatory process
(Cossent, 2013). This includes specifying the regulatory asset value of the firm's capital
assets (also known as the "rate base"), tracking depreciation of assets, calculating the
weighted average cost of debt and equity capital sources (WACC), allowances for taxes
paid, and regular auditing of utility investments and accounts. Regulatory accounting
systems must be carefully designed to collect any information necessary for adequate
regulatory decisions, while avoiding excess burdens on regulated firms and regulatory
staff. Accounting should also be auditable and traceable to minimize opportunities for
strategic behavior. In addition, regulators frequently employ outside consultants in this
process to help review and assess the regulated firm's performance.
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Another way in which regulators can reduce their information disadvantages vis-a-vis
the utility (and help reduce the opportunity for strategic behavior) is by comparing the
utility's actual costs and performance relative to a reference or benchmark of
performance (Cossent, 2013; G6mez, 2013b; Jamasb & Pollitt, 2001, 2003). This
reference or benchmark can be the actual performance of one or more similar firms
(known as "yardstick competition"), an estimate of efficient performance derived
through statistical analysis of the measured performance of a large number of similar
firms ("frontier benchmarking"), or an estimate of efficient performance derived from
engineering models ("reference network" or "engineering norm" approaches).
Consider the simple case where two identical firms serve two identical service
territories, but do not directly compete with one another. By establishing allowed
revenues for both firms based on some weighted average of both firms' costs, the
regulator can force these two firms to effectively compete with one another. If one firm
cuts costs faster than the other, it will profit under this scheme, while a firm that lags
behind its twin will incur a loss. Shleifer (1985) demonstrates the efficiency of this
method of yardstick competition and notes that even in the more realistic case of
heterogeneous firms, yardstick competition can compare favorably to setting allowed
revenues based on the firm's cost-of-service alone, provided that the heterogeneity can
be observed and accounted for. In addition, the regulator does not need to know the
precise methods employed by each firm to reduce costs and improve X-efficiency.
Rather, regulatory accounting data on each firm's performance is sufficient, thus
reducing information asymmetries and regulatory burden.
Alternatively, the regulator does not need to limit its comparisons to the performance
of a few similar firms. Instead, through statistical and econometric frontier
benchmarking techniques, the regulator can analyze the relative efficiency and
performance of a large number of utilities in the sector, both within the same country or
even internationally (Jamasb & Pollitt, 2001, 2003). Using these benchmarking
techniques, 5 regulators can estimate "the efficient frontier"-that is, the optimal ratio
of inputs to performance, controlling for various relevant characteristics or cost drivers
(i.e., load factors, density of loads, geographic constraints, etc.)-and then benchmark
the firm's actual performance relative to this frontier.
Finally, regulators can employ engineering reference or norm models to construct an
ideal or efficient representative firm, which can be compared to performance of the
actual regulated firm (Cossent, 2013; Domingo, et al., 2011; Jamasb & Pollitt, 2008).
Using a combination of engineering models and optimization methods, these methods
s For reviews of benchmarking techniques for utility regulation, see Cossent (2013), Gomez (2013b), and
Jamasb & Pollitt (2003 & 2001).
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Jesse D. Jenkins (2014)
can construct a realistic, efficient reference network, including estimated investment
and maintenance costs as well as energy losses and quality of service levels, taking into
account the particularities of the real regulated firm's service territory (i.e., the location
and profiles of network users, cost and performance of available technology
components, and geographic constraints on network layout). The key parameters of this
reference network can then be employed as indicators of efficient performance for the
real, regulated firm.
1.2 - Emerging Challenges in the Regulation of Electricity Distribution Utilities
The regulation of the electric power sector has historically co-evolved along with the
underlying technical and economic characteristics of the electricity system.6 Economies
of scale and scope driven by the particular engineering characteristics of conventional
thermal and hydro power plants and electricity transmission and distribution networks
gave rise to natural monopoly characteristics and prompted the emergence of the
regulatory methods discussed in Section 1.1. Over time, a number of technological
changes occurred, including the advent of less capital intensive and smaller scale natural
gas-fueled power plants, much stronger and interconnected transmission networks,
computerized handling of consumer information, industrial co-generation opportunities,
and renewable energy technologies. This new portfolio of technologies reduced
economies of scale and lowered barriers to entry, making competitive wholesale
generation and retail supply markets possible. This era of technological innovation thus
sparked a wave of industry restructuring, liberalization, and regulatory reform that
spread across numerous jurisdictions during the 1980s and 1990s 7 (Bushnell &
Borenstein, 2000; Perez-Arriaga, 2013).
Today, a new era of technological innovation is once again reshaping the electric power
sector in many jurisdictions, with much of that change centered around the electricity
distribution sector (Bharatkumar et al., n.d.). Previous eras of regulatory reform have
focused on market designs (particularly for wholesale generation and retail supply) and
regulatory incentives for the network-related activities (transmission and distribution)
to improve economic efficiency, affordability, and quality of supply. A new set of
reforms must further these ongoing objectives while also addressing the evolving uses
of distribution networks and fostering technological innovation and the efficient
evolution of electric power systems and markets (Bauknecht, 2011; Bharatkumar et al.,
6 This section is based in part on Chapter 3, "Regulatory Issues," in Bharatkumar et al. (2014).
7 Earlier reforms include the restructuring of the Chilean electricity market in 1981, introduction of
independent power producers in the United States via the Public Utilities Regulatory Policy Act (PURPA)
of 1978.
15
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
2014; Cossent, G6mez, & Frias, 2009; Cossent, 2013a; Lo Schiavo et al., 2013; Newcomb,
Lacy, & Hansen, 2013; Perez-Arriaga et al., 2013).
In particular, a broad range of distributed energy resources (DERs), including distributed
generation and storage, demand response, and electric vehicles, are proliferating in
electricity distribution networks. Information and communication technologies (ICT) are
also becoming embedded across the distribution system, enabling new "smart grid"
capabilities. Together, these new technologies and capabilities are leading to the
emergence of innovative business models utilizing distributed energy systems, or
DESs-novel systems combining one or more DERs with ICT capabilities to deliver value
to electricity end-users, upstream market actors, and/or system operators (Bharatkumar
et al., n.d.). These emerging technologies and associated business models have the
potential to drive several paradigmatic shifts in the planning and operation of electricity
distribution systems and related markets:
" The traditional top-down power flow from centralized generation sources
connected to the high voltage transmission grid through lower-voltage
distribution to end consumers is challenged by local distributed generation (DG),
requiring changes to distribution infrastructure and operations. Multi-directional
power flows across distribution networks may soon be the norm, and new
opportunities are emerging for local means of electricity trade, including
microgrids and on-site DG, that directly compete with wholesale generators.
*
Distributed storage (DS) could entail profound changes to the real-time
operation of electric power systems, offering a buffer between system supply
and demand, new ways to provide ancillary services to network operators, and
opportunities to temporally shift energy supply to maximize the value of energy
production and meet peak demands. At the same time, cost effective storage
combined with on-site generation may also reduce electricity end user's reliance
on distribution networks.
*
Demand response (DR) makes electricity loads far more responsive to economic
and operational signals than ever before. Conversely, the proliferation of
customer-owned DG, particularly variable distributed solar and wind
technologies, may make generation less controllable and predictable for system
operators.
*
Widespread adoption of electric vehicles (EVs) would constitute an important
new class of electricity system users and loads. EVs also hold the possibility of
injecting power back into the grid, delivering so-called "vehicle-to-grid" (V2G)
services. Efficient price signals and/or new control systems will be essential to
16
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
manage and coordinate EV charging and V2G services, while new network
investments must accommodate and enable EV users. The role of the
distribution utility in enabling this new market segment must also be defined.
Last, but perhaps most significantly, a cost-effective combination of DG, DS, DR
and EVs-whether in the format of an autonomous or semi-autonomous
physical microgrid or as a virtual decentralized aggregation of each of these
components-may challenge the current centralized paradigm of the electric
utility, initiating a transition towards a more decentralized structure and
organization of the power sector.
Many of these trends are still nascent. Other important changes, foremost the
increasing penetration of solar photovoltaics and other DG, are already established facts
and concern distribution utilities and regulators in many jurisdictions today. In addition,
while many of these trends have so far been driven primarily by supportive public policy
measures, utilities and analysts are increasingly focused on the potential for selfsustaining and disruptive forces to take hold. As technology innovation and industry
maturation and scale-up steadily improve the price and performance of these
technologies, tipping points are possible, after which adoption may proceed quite
rapidly (Bharatkumar et al., 2014; Eurelectric, 2013b; Kind, 2013; Newcomb et al., 2013).
The ongoing evolution of electricity distribution networks and emergence of new
network users therefore require a new era of proactive regulatory innovation and
reform. These reforms will likely include:
" Updating remuneration methods to ensure distribution utilities are
compensated for investments required to accommodate new DERs and DESs and
incentivized to make efficient use of these same resources to improve system
reliability, reduce losses, and defray unnecessary capital expenditures.
*
Redesigning distribution access or use of network charges to provide efficient
price signals for the location and operation of new DERs while fairly allocating
costs to network users and ensuring adequate cost recovery for the distribution
utility.
*
Revisiting the proper industry structure and clarifying the responsibilities of the
distribution utility, including vis-&-vis ownership or operation of DER assets,
management of and access to data generated by new ICT systems, and
installation, maintenance and operation of electric vehicle charging systems.
17
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
*
Clarifying the ways in which the distribution utility and adjacent market actors,
including retailers, transmission system operators, and new DES-related
businesses (e.g. aggregators), will interact and coordinate to dispatch DERs.
*
Considering the design of mechanisms to incentivize long-term investments in
network innovation, technology development, and learning, not just short-run
efficiency and cost-savings.
While each of these important challenges requires proactive regulatory innovation, only
the remuneration challenge lies within the scope of this thesis.
The evolving nature of the distribution sector will entail new customer demands and
uses for the distribution system, new DER and ICT-related cost drivers for the utility, and
new opportunities to harness emerging technologies and services to reduce distribution
system costs and improve quality of service. These changes will exacerbate several of
the fundamental challenges in the economic regulation of distribution utilities
introduced in Section 1.1.
First, large-scale penetration of DERs within distribution utilities networks will likely
increase the total costs of business-as-usual management of the distribution system
(that is, a continued "fit-and-forget" grid management strategy) (Cossent, G6mez, &
FrIfas, 2009; Cossent et al., 2010). Substantial future investments will be required to
fulfill the distribution utility's open access requirements and connect all new DER
system users as well as to enable the system to deal with bidirectional load flows,
potentially increased volatility in peak demand, and new DG-related system peaks at
various voltage levels. Unless these new costs are adequately remunerated, distribution
utilities will have strong incentives to block, delay, or oppose new uses of their network.
Regulators must therefore be equipped to proactively identify the impacts of new DERrelated network uses on distribution costs and to incentivize utilities to accommodate
DERs through appropriate remuneration methods.
Second, distribution utilities can also achieve important cost savings by adopting an
active system management approach, especially as DG shares increase (Cossent et al.,
2009, 2011; Eurelectric, 2013a; Olmos et al., 2009; Poudineh & Jamasb, 2014; Trebolle,
et al., 2010). Setting up ICT and advanced grid management infrastructure that allows
distribution utilities to more actively manage distribution network configuration and
make use of DESs for their daily grid operations will entail substantial upfront capital
expenditures (CAPEX) but can also improve system operating efficiency, avoid or defer
other CAPEX, and reduce operational expenditures (OPEX) (Poudineh & Jamasb, 2014;
Trebolle et al., 2010). Likewise, coordination and management of EV charging within an
active system management approach is essential to avoid heavy investments into low18
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
and medium-voltage lines to compensate for local peak demand resulting from high
penetration of EVs (Fernandez, et al., 2011). Remuneration must therefore provide
incentives for distribution utilities to take efficient advantage of new DER and ICTrelated capabilities and to manage important tradeoffs between CAPEX and OPEX to
find the efficient balance between these expenditures.
Third, as the sector evolves, distribution utilities are likely to develop more intimate and
immediate knowledge about new cost drivers and opportunities than the regulator,
heightening information asymmetries and creating new opportunities for strategic
behavior. Regulatory tools to overcome the regulator's information disadvantages will
thus become even more critical as the distribution sector evolves. Furthermore, while
frontier benchmarking and yardstick approaches are often employed for these
purposes, these approaches will become much more challenging as the sector evolves.
For example, the growth of distributed generation introduces much more heterogeneity
between distribution network costs. The availability of solar, wind, biomass/biogas, and
combined heat and power resources differs substantially from location to location and is
likely to lead to divergent evolution of distribution networks in different regions. In
addition, as network uses and drivers of cost rapidly evolve, benchmarking based on
past utility performance or cost will no longer provide an accurate estimate of the
forward-looking efficient frontier. New forward-looking methods to assist the regulator
in determining appropriate allowed revenues while overcoming information asymmetry
are thus essential.
Finally, the evolving nature of distribution system technologies and uses will further
exacerbate the inherent uncertainty facing the regulator. This is a challenge particularly
for ex ante remuneration methods, such as revenue caps. As uses of the network and
the new technologies available to utilities may evolve quite rapidly, network costs may
deviate substantially from ex ante regulatory estimates, leading to two types of error:
forecast error and benchmark error. Costs may rise or fall unexpectedly due to new
network uses (e.g., the rapid penetration of newly subsidized or newly cost competitive
DG), an example of forecast error. Alternatively, the regulator may fail to anticipate the
emergence of new cost saving technologies within the regulatory period, leading to
benchmark error. In either case, regulators employing ex ante remuneration methods
may be at greater risk of either violating the firm participation constraint if cost recovery
is too low or leaving significant economic rents to the utility by being too generous in
setting the ex ante revenue cap. More frequent ex post reviews and adjustments to
remuneration levels can address these challenges, but not without a cost. Frequent ex
post revisions of remuneration levels and reopeners of the regulatory contract can
create significant regulatory uncertainty and thus may raise the cost of capital for
distribution utilities as well as potentially undermining efficiency incentives.
19
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
1.3 - New Solutions for Regulation of Distribution Utilities Under High Penetrations
of Distributed Energy Resources
Addressing the combination of regulator challenges discussed in Section 1.2 will require
regulators to more widely adopt best practices in network utility regulation. In
particular, this thesis proposes a novel process for establishing the allowed revenues of
an electricity distribution utility and demonstrates its application as a practical solution
to the imminent regulatory challenges discussed above.
The proposed method is a combination of three established best practices or "state of
the art" regulatory tools: an engineering-based reference network model (RNM) for
forward-looking benchmarking of efficient network expenditures (Cossent, 2013;
Domingo et al., 2011); an incentive compatible menu of contracts to elicit accurate
forecasts from the utility and create incentives for cost saving efficiency efforts (Cossent
& G6mez, 2013; Cossent, 2013; Crouch, 2006); and ex post automatic adjustment
mechanisms, or "delta factors," to accommodate uncertainty in the evolution of
network use and minimize forecast error.' This section summarizes the overall process
at a conceptual level and introduces its advantages. Chapter 3 will demonstrate in detail
the step-by-step implementation of this process to establish the allowed revenues of a
realistic simulated distribution network utility and evaluate its performance.
The proposed regulatory process involves ex ante calculation of allowed revenues and
establishment of clear rules for ex post evaluation of actual expenditures and
adjustments to final allowed revenues. Figure 1-2 depicts the ex ante regulatory process
carried out at the beginning of each regulatory period. The key steps are as follows:
1. The process begins with the utility submitting to the regulator a detailed year-byyear forecast of the evolution of network uses over the upcoming regulatory
period (Step 1).9 This forecast should at minimum include a set of appropriately
justified scenarios covering a range of the likely load and DER penetration levels,
including discussion of the most likely geographic evolution of loads and DERs.
8
This general method is first proposed in Cossent (2013a) Chapter 5 and Cossent and G6mez (2013) and is
developed further herein, including demonstration of implementation in Chapter 3. The methods herein,
particularly the menu of contracts approach, also draw on the practical experience of the UK Office of Gas
and Electricity Markets (Ofgem), as published in Ofgem (2009, 2010b, 2013a, 2013c) and related methods.
The use of annual automatic adjustment factors to account for deviations from forecasted load growth
and other network uses is also demonstrated.
I This general method is agnostic as to the length of the regulatory period, although this length should be
carefully determined to balance the appropriate incentives for cost savings on the one hand and
adjustments to mitigate uncertainty on the other hand. In general, the profit and risk sharing mechanism
established by the proposed menu of contracts along with use of adjustment factors should reduce the
impacts of uncertainty and enable a longer regulatory period.
20
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
Figure 1-2: Proposed regulatory process - ex ante actions
Ex ante process
(performed only at beginning of regulatory
Regulator
1.
Submission of utility's forecast for evolution of network uses
2.
Analysis of utility forecast / stakeholder comments
3.
Regulator's comments on utilit forecast
I
4.
Revisions to utility forecast
AI
5.
Final forecast for evolution of network uses
6.
Establishment of regulator's ex ante TOTEX estimate
7.
Construction of incentive compatible menu of contracts
8.
Submission of utility investment plan and estimated TOTEX
9.
Calculation of ex ante TOTEX and revenue baselines and ex
post sharing factor
Utility
a
10. Calculation of automatic adjustment factors ("delta factors")
to accommodate uncertainty in forecasted network use
11. Final regulatory contract published
2. The regulator then critically reviews this forecast (Step 2). This review may also
include a period of open comment on the preliminary forecast by stakeholders.
3. At the conclusion of this review, the regulator will submit clear comments to the
utility on required changes or further analysis needed to construct a final
forecast (Step 3).
4. Upon receiving this feedback, the utility will then perform any required updates
to their scenarios (Step 4)
5. The utility then re-submits a final forecast to the regulator for use throughout
the remainder of the regulatory process (Step 5).
6. After receiving the final forecast from the utility, the regulator will then
construct their estimate of efficient network expenditures to meet the
forecasted evolution of network use over the regulatory period (Step 6). In this
21
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
stage, the regulator will employ a reference network model (RNM), a large-scale
distribution network planning tool capable of producing an estimate of efficient
costs of expanding and maintaining a network to serve a specified set of network
users at prescribed quality levels (i.e., maximum statistical probability of network
disruptions, voltage limits, etc.) and considering incentives for reduction of
network losses. The RNM described in Domingo et al. (2010) will be used
throughout this thesis to demonstrate application of this method, but any
suitably rigorous RNM could be employed. The use of an RNM gives the
regulator a forward-looking benchmark for efficient network expenditures,
helping reduce information asymmetry and manage uncertainty.10 In this stage,
the regulator produces their estimate of efficient TOTEX for the regulatory
period. In addition, the regulator can now determine the expected portion of
TOTEX associated with both CAPEX (including incremental and replacement
investments) and OPEX (network maintenance). These shares are used to fix the
portion of realized TOTEX that is capitalized into the utility's regulated asset
value (or "rate base") used to determine allowed revenues during the ex post
process. This TOTEX-based approach to capitalization equalizes incentives for
cost-savings across both CAPEX and OPEX, which can be distorted using a CAPEXonly approach to capitalization (see Chapter 3, Section 3.2 for more).
7.
The next step involves creation of an incentive compatible menu of profitsharing contracts for the utility (Step 7).11 A menu of contracts specifies an ex
ante regulatory allowance as well as a clear rule for ex post evaluation of actual
expenditures and adjustments to final remuneration. The menu outlines a
continuum of profit-sharing factors (sliding-scale efficiency incentives) wherein
values depend on the ratio of the utility's estimate of network costs over the
regulatory period to the regulator's estimate derived via use of the RNM in Step
10 Note that as with any other benchmarking method, the cost figures produced by the RNM should not
be used directly to establish the utility's allowed revenues. The RNM is a model that simplifies reality and,
as a result, generally produces a network solution that is less expensive than an efficient real-world utility
is likely to achieve. For example, the RNM can plan the network with perfect foresight given the input
forecast, while utility planners may have to adjust plans over time, incurring sunk costs as real-world
conditions change. Other constraints faced by a real utility company may also be ignored by the RNM.
Regulators should therefore adjust the cost estimates produced by the RNM accordingly. The RNM's
performance can be benchmarked by running it against several real world efficient network cases with
known expenditures to estimate an appropriate correction factor, or the regulator can employ
consultants to 'spot check' the estimates produced by the RNM.
1 Cossent and G6mez (2013) describe a practical method for creation of an incentive compatible menu of
contracts, and this thesis builds on that work herein. Additionally, this general approach has been
successfully implemented by the Ofgem since the fourth distribution price control review (DPCR4)
enacted from 2005-2010 and is now an integral part of Ofgem's RIO framework (Ofgem, 2010c). The UK's
approach, known as the Information Quality Incentive (IQI) is described in Crouch (2006) as well in Ofgem
(2009, 2010a, 2013c) and Cossent and G6mez (2013).
22
Jesse D. Jenkins (2014)
Chapter 1: Economic Regulation of Electricity Distribution Utilities
6. As discussed in Section 1.1, if the design of the menu preserves incentive
compatibility, it will provide incentives for the utility to provide a truthful
estimate of expected network costs, helping overcome information
asymmetries. In addition, incentives for the utility to engage in strategic behavior
by inflating estimates of network costs are eliminated. An example menu of
contracts is shown in Table 1-3 below.
Table 1-3: Example incentive compatible menu of profit-sharing contracts
Shaded cells correspond to those for which the ex ante utility forecast matches actual expenditures, demonstrating
the incentive compatible nature of this matrix. For any realized value of network costs (i.e. horizontal row in the
bottom half of the matrix), the utility will earn the greatest revenues in the case where their realized cost matches
their ex ante forecast. Efficiency incentives are also preserved, as lowering realized costs below the utility's forecast
(i.e. moving up in a vertical column) will increase the utility's final revenues (and vice versa). This menu uses the
following discretionary parameters (see chapter 3, Section 3.3 for details): weight on regulator's estimate = 0.66;
Reference sharing factor = 0.7; Sharing factor rate of change = -0.01; Reference additional income = 1.0%.
Ratio of firm's cost estimate to
regulator's cost estimate
Allowed revenues ex ante
%of regulator's cost estimate]
Sharing factor [%]
Additional Income
[% of regulator's cost estimate]
Ratio of realized ex past
expenditures to regulators ex
ante estimate [%J
85
90
954.5
100
105
110
115
120
125
90
95
100
105
110
115
120
X,,.,,
96.6
98.3
100.0
101.7
103.4
105.1
106.8
SF
80.0
75.0
70.0
65.0
60.0
55.0
50.0
Al
3.2
2.2
LO
-0.2
-1.5
-2.9
-4.4
L.
.
R%
Final ex post adjustment to allowed revenues [%of regulator's ex ante estimate]
8.1
9.5
10.6
11.
12.1
12.5
5.4
6.5
7.4
8.0
8.4
2.6
3.5
4.1
4.5
-0.1
0.5
0.9
.9
05
2.9
2.5
-2.5
-2.9
-3.5
6
-5.6
-6.0
-6.6
-7.5
-8.5
-8.9
-9.5
-10.4
-11.5
-11.1
-11.5
-12.1
-13.0
-14.1
-15.5
-13.9
-14.5
-154
-16.5
-17.9
-19.5
Ap a
6.5
4.0
1e5
-1.0
-3.5
-6.0
-8.5
-13.5
8. Once the menu of contracts is established and published, the utility will then
develop their detailed year-by-year investment or business plan for the
regulatory period (Step 8). This plan should justify the utility's expected
expenditures in light of the final forecast created in Steps 1-5 including expected
responses to departures from the central forecast. This plan should outline not
only the expenditures but also the expected impact on network users, including
quality of service, losses, reliability, and ability to interconnect DERs, as well as
other important performance metrics (i.e. environmental metrics, innovation,
etc.). The idea, as articulated by Ofgem, is for the business plan to clearly outline
the "value for money" network users should expect over the regulatory period
(Cossent & G6mez, 2013; Ofgem, 2010b, 2010c, 2013b, 2013c).
23
Jesse D. Jenkins (2014)
Chapter 1: Economic Regulation of Electricity Distribution Utilities
9. Next, the regulator can determine the ex ante TOTEX baseline (the regulator's
estimate of the efficient frontier for this utility), calculate the allowed revenue
baseline, and publish the ex post efficiency incentive (sharing factor) for the
utility. The TOTEX baseline is determined by applying a weighted average of the
regulator's estimate of the efficient frontier (based on the RNM in Step 6) and
the utility's estimate (submitted in their business plan in Step 8). The weighting
of the two estimates is specified by the regulator as part of the creation of the
menu of contracts. This TOTEX baseline is the final ex ante estimate of efficient
expenditures against which realized expenditures are compared to determine
the efficiency incentive the utility earns (or incurs, in the case of over-spending).
Next, the ratio between the utility's estimate of TOTEX and the regulator's
estimate determines the sharing factor and additional income allowance for the
utility as defined by the menu of contracts produced in Step 7. Finally, an ex ante
allowed revenue baseline is calculated using financial accounting to determine
the appropriate revenues necessary to remunerate the utility for operational
expenditures (OPEX) as well as depreciation and cost of capital allowances based
on the regulated asset value. The utility then has a clear expectation of how their
revenues will evolve over the regulatory period, providing strong regulatory
certainty and clear incentives for efficient management of network costs.
Table 1-4 Example of TOTEX and revenue baseline calculations
Menu of contract corresponds to example in Table 1-3. See Chapter 3, Section 3-4 for detailed
methodology.
Year1
Year2
Year3
Year4
Year5
NPV
Regulator's estimate
$16.40
$16.81
$17.24
$17.67
$18.11
$71.44
Utility's estimate
$19.69
$20.18
$20.68
$21.20
$21.73
$85.73
TOTEX ESTIMATES
TOTEX AND REVENUE BASELINE AND MENU OF CONTRACTS PARAMETERS
TOTEX baseline
$17.52
$17.96
$18.41
$18.87
$19.34
$76.30
Revenue baseline
$32.03
$32.18
$32.37
$32.58
$32.82
$134.5
Ratio
1.2
Sharing Factor
50%
Additional income
-$3.14
10. While the use of an RNM and menu of contracts produces a clear revenue
determination for each utility taking into account forecasted cost of capital,
evolution of network uses, and network component costs, the ex ante nature of
this regime means there will always be uncertainty regarding the accuracy of
these forecasts. To manage this uncertainty and mitigate the impacts of forecast
error, the regulator next employs the RNM to estimate network costs across a
range of uncertainty scenarios that capture the likely range of potential
evolution of load, DG penetration, or other important and uncertain cost drivers
24
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
(Step 10). Regression analysis of the resulting estimated network costs can then
be used to determine the relationship between deviations in cost driver values
and total network costs. These coefficients, which this thesis calls "delta factors,"
prescribe simple formulas to adjust the estimated TOTEX baseline ex post based
on the realized evolution of network uses. This use of delta factors minimizes the
impact of forecast error and thus reduces the risk that the revenue
determination will need to be re-opened, increasing regulatory certainty.' 2
These delta factors also reduce incentives for the utility to slow interconnection
of DERs by ensuring cost recovery even if DER penetration grows more rapidly
than expected.
11. The ex ante regulatory process is now complete, the regulator publishes the final
regulatory contract for the utility for the duration of this regulatory period
(Step 11).
The ex ante regulatory process is now complete and the regulatory period begins.
At the conclusion of each year during the regulatory period, an ex ante regulatory
commences to adjust the utility's allowed revenues in light of the realized evolution of
system uses and utility expenditures. Figure 1-3 illustrates this annual ex post regulatory
process, which is comparatively simple.
Figure 1-3: Proposed regulatory process - annual ex post actions
Regulator
Utility
A.
ULIIILY makes expenditures and reportsto regulator
2.
Audit of TOTEX and automatic adjustments to TOTEX baseline
computed due to deviation from forecasted network use
3.
Efficiency incentive computed as per menu of contracts and
adjustments made to allowed revenues
4.
Forward-looking revenue adjustments computed to true up
revenues
5.
Adjusted revenue allowance published
TV
2 Similar mechanisms have been employed by the Comisi6n Nacional de Energia (CNE) in Spain to
account for deviations in load growth-the "Y factor," calculated using an RNM (Cossent & G6mez,
2013)-and by Ofgem in the UK to account for DG penetration during DPCR4 and DPCR5-the "DG
incentive." The method used to calculate of the adjustment factor is not transparent (Ofgem, 2010a).
25
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
1. First, the utility submits a detailed report on actual investment and operational
expenditures (the utility's realized TOTEX) as well as details on the evolution of
system uses (i.e., load growth and penetration of DER) (Step 1).
2. Next, the regulator will audit these reports to ensure their accuracy, and then
compute the automatic adjustments to the ex ante TOTEX baseline to account
for any differences in actual network use as compared to the ex ante forecast
(Step 2). The delta factors for each key network use (i.e., load growth and DER
penetration) are multiplied by the difference in realized network use from the
forecast, producing an adjustment to the net present value (NPV) of efficient
TOTEX. Since the utility is not expected to make all of the expenditures to
accommodate this deviation from forecast in the immediate year, this total NPV
adjustment is then converted into a stream of annual adjustments to the TOTEX
baseline for the remaining years in the regulatory period.
3. After calculating the adjusted TOTEX baseline, the regulator then compares the
utility's realized TOTEX over the last year with the adjusted TOTEX baseline, and
the efficiency incentive is calculated by multiplying the firm's sharing factor
(from the menu of contracts) by the difference between the adjusted TOTEX
baseline and the realized TOTEX (Step 3). The efficiency incentive is the portion
of the realized over/under-spend shared by the utility's shareholders. The
utility's ex post allowed TOTEX is thus the utility's realized TOTEX less this
efficiency incentive. Next, the regulator computes the revenue allowance based
on this allowed TOTEX, including allowed maintenance expenses and allowances
for the depreciation of the regulated asset value and payment of debt and equity
(cost of capital allowance).
4. Since revenues have already been collected over the course of the recently
concluded year, the regulator must adjust the utility's revenue allowance in
future years to "true up" the collected revenues and the ex post revenue
allowance computed above (Step 4). The deficit or surplus in collected revenues
is calculated as the difference between allowed cost recovery and collected
revenues. Next, an annual stream of adjustments to true up future allowed
revenues is calculated so as to ensure that the NPV of adjustments to future
revenues corrects for the surplus or deficit in collected revenues over the
recently concluded year. This true up is applied as a stream of annual
adjustments, rather than a single lump sum correction, so as to smooth the
impact on rates and avoid discontinuous rate increase/decreases.
26
Jesse D. Jenkins (2014)
Chapter 1: Economic Regulation of Electricity Distribution Utilities
5. Finally, the regulator publishes the adjusted revenue allowance (Step 5),
including the true up adjustments calculated above, and the regulatory period
continues.
Table 1-5 below illustrates an example of the final profits (as net earnings before
interest and taxes) and return on equity for a range of realized network expenditures
using this approach. This table demonstrates the incentive compatibility of the full
regulatory process, as the utility makes the most profit whenever realized expenditures
align with firm's ex ante estimate of efficient costs (the shaded cells). Additionally, the
firm can always increase their net profits by reducing costs below their ex ante estimate,
demonstrating the efficiency incentives created by this approach.
Table 1-5: Example of final profit and return on equity across a range of realized expenditures
EX ANTE TOTEX ESTIMATES
Regulator Estimate
NPV M$
$71.4
$71.4
$71.4
$71.4
Firm's Estimate
NPV M$
$64.3
$71.4
$78.6
$85.7
%
90
100
110
120
Ratio (Firm/Regulator)
EX ANTE REGULATORY CONTRACT (w = 0.66; SFref = 0.7 SFro = -0.01; Alref = 1.0)
TOTEX baseline
NPV M$
$69.0
$71.4
$73.9
$76.3
Revenue baseline
NPV M$
$136.3
$135.9
$135.3
$134.5
%
80
70
60
50
NPV M$
$2.3
$0.7
-$1.1
-$3.1
Sharing Factor
Additional Income
EX POST RESULTS
Realized TOTEX
(NPV M$)
TOTEX Ratio
(Realized/
Regulator)
$64.3
90
$71.4
100
$20.36 / 6.58
$78.6
110
$16.67 / 5.38
$17.44 / 5.60
$85.7
120
$12.99 / 4.18
$14.24 / 4.56
Earnings Before Interest and Taxes (NPV M$) /
Return on Equity (%)
$23.83/ 7.70
$23.16 / 7.47
$22.00/ 7.10
$20.44 / 6.57
$19.76 / 6.35
$17.53 / 5.61
$15.00 / 4.79
This proposed regulatory method equips the regulator with several tools to confront the
regulatory challenges created by the evolution of the electricity distribution sector and
the increasing penetration of distributed energy resources.
The regulatory regime proposed herein helps overcome information asymmetry by
equipping the regulator with a reference network model with which to develop their
estimate of efficient network expenditures. The RNM emulates the network planning
27
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
practices of an efficient utility and can help the regulator develop more accurate
estimates of future network costs given the expected evolution of network uses.
Additionally, as the model can be designed to accommodate expected evolutions in
network use, technology costs, and network management practices, the RNM creates a
forward-looking benchmark for efficient total network expenditures (TOTEX), reducing
the uncertainty facing the regulator. In effect, the RNM gives the regulator a tool with
which to "peer into the future," a crucial ability in ex ante regulatory approaches. This
forward-looking capability stands in contrast to statistical benchmarking techniques,
which rely on backward-looking analysis of realized expenditures during prior regulatory
periods and thus cannot capture the dynamic changes now unfolding in the electricity
distribution sector. The RNM can also be used to explore a range of possible scenarios
for the evolution of network uses (i.e., load growth and DER penetration). The model
results can then be used to compute delta factors, simple formulas to automatically
adjust the efficient TOTEX baseline in light of the realized evolution of network use.
These delta factors effectively minimize the impacts of forecast errors, a significant
advantage given increased uncertainty about the likely evolution of network use over
the coming years.
Combining the use of an RNM with an incentive compatible menu of contracts further
reduces information asymmetry by incentivizing the utility to submit their most accurate
estimate of future network expenditures. The incentive compatible property of the
menu of contracts thus eliminates incentives for the utility to engage in strategic
behavior by inflating their estimate of necessary TOTEX, a significant advantage over
other ex ante regulatory approaches that do not employ a menu of contracts. The profit
sharing parameter established by the menu of contracts creates clear incentives for the
utility to seek cost-saving efficiency measures throughout the regulatory period. This
profit sharing incentive gives the utility's management and shareholders a direct stake
in cost-saving measures and thus overcomes the moral hazard problem that plagues
cost-of-service regulation. Furthermore, the regulator has the flexibility to establish the
desired strength of the efficiency incentives to manage the tradeoffs between allocative
efficiency (extracting rents from the utility) and X-efficiency (providing incentives for
cost savings). Finally, by selecting the strength of the profit sharing factor, the regulator
also has the ability to mitigate the impacts of benchmark error - i.e., an error in the
regulator's estimate of efficient TOTEX (irrespective of the evolution of network use).
The lower the sharing factor, the closer the regulatory contract becomes to a cost-ofservice contract, and thus the less sensitive the firm's profits are to differences in
forecasted and realized costs, and vice versa. The regulator can thus select an
appropriate sharing factor based on their confidence in the accuracy of their forecasts of
efficient network expenditures.
28
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
Note that the method described above only considers the establishment of allowed
TOTEX and the primary allowed revenues. This regulatory process must be combined
with careful consideration of performance-based metrics for quality of service and other
desired outputs such improvements in reliability, reduction of losses, achievement of
environmental objectives. See Cossent (2013a), Malkin & Centolella (2013), and Ofgem
(2010c) for more on output or performance-based incentives.
Furthermore, while a well-designed menu of contracts provides strong incentives for
efficiency and will encourage the utility to pursue novel and innovative approaches to
network investment and management, additional incentives for long-term innovation
may be necessary. Ex ante regulation of revenues with strong sharing-factors can place
the short-run expenditures needed to unlock long-term innovations at odds with the
utility's incentives for cost-savings (Bauknecht, 2011). When these dynamics are
combined with the considerable knowledge spillovers, long time horizons, and inherent
risks associated with the development of new network technologies and demonstration
of novel practices (Lester & Hart, 2012; Lo Schiavo et al., 2013), this can lead to a
substantial under-investment in network innovation. Regulators may therefore need to
employ explicit incentives for innovation, including input-based incentives (such as an
R&D cost pass-through), output-based incentives (financial incentives for adoption rates
of novel technologies or practices), or competitive innovation funds (such as the UK's
Low-carbon Innovation Fund). For discussion of network innovation incentives, see
Bauknecht (2011), Lester & Hart (2012), Lo Schiavo et al. (2013), and Ofgem (2010c).
Unfortunately, both of these important aspects of network regulation lie outside the
scope of this thesis.
1.4 - Structure of the Thesis
The remainder of this thesis demonstrates the regulatory process introduced above in
detail. First, Chapter 2 describes the simulation of a realistic, large-scale electricity
distribution network for use in demonstration of the novel regulatory method proposed
herein. Using a reference network model, a base network is constructed for a roughly
120 square kilometer region of Denver, Colorado. A range of scenarios for the evolution
of network uses is then developed and the network is expanded to accommodate the
realized network uses in each scenario. The estimates of network costs (CAPEX and
OPEX) produced by this simulation are then used throughout Chapter 3, which
demonstrates, step-by-step, the implementation of this regulatory process. The key
stages of the regulatory process are described in detail and the performance of the
regulatory process is evaluated.
29
Chapter 1: Economic Regulation of Electricity Distribution Utilities
Jesse D. Jenkins (2014)
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Reform: What Have We Learned (Forthcoming). Chicago, IL: University of Chicago
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low-cost, low-carbon energy system. Cambridge, Mass.: MIT Press.
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Jesse D. Jenkins (2014)
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34
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
2 - Constructing a Simulated Electricity Distribution Network
This chapter describes the simulation of a realistic, large-scale urban distribution network
used to demonstrate the novel regulatory process proposed in this thesis. The
methodology employed for this simulation was originally developed in Vergara, et al.,
(2014) and employs the reference network model (RNM) described in Domingo et al.,
(2011). This thesis modifies and extends these methods to create a realistic simulated
network for a roughly 120 square kilometer (km-sq) section of Denver, Colorado,
encompassing more than 27,000 individual load points and approximately 468,000
kilowatts (kW) of peak load (see Table 2-1). Several scenarios for the growth of this
network are also simulated, capturing the network expansion, reinforcements, and
maintenance costs necessary to accommodate a range of possible increases in loads and
the penetration of distributed solar photovoltaic (PV) generators.
Table 2-1: Characteristics of simulated network
Location
Denver, Colorado
Simulation area
120.3
km-sq
Population density
1,580.5
persons/km-sq
Estimated population
190,187
Persons
Estimated load power density - Base network
Estimated peak power demand - Base network
3,890
kW/km-sq
468,079
kW
Load Points - Base Network
LV
Industrial
MV
HV
Total
0
212
42
254
Commercial
6,263
1,274
63
7,600
Residential
18,788
637
0
19,425
Total
25,051
2,123
105
27,279
2.1 - Specification of Simulation Parameters
The creation of the simulated distribution system begins with the specification of key
simulation parameters describing the composition and characteristics of network users
(loads and DG) and other characteristics of the network.
The first set of parameters specifies the power density of loads within the simulated
network and the average peak active power and average reactive power factor for loads
at each voltage level (Table 2-2). The power density parameters are based on the
population density of the simulation location. The population density of Denver is 1,580.5
persons per km-sq (U.S. Census Bureau, 2011) and there are 2.3 persons per habitation in
the city (U.S. Census Bureau, 2014). At an average peak demand of 1.84 kilowatts (kW)
35
Jesse D. Jenkins (2014)
Chapter 2: Constructing a Simulated Electricity Distribution Network
1
per habitation, consistent with the typical residential customer served by Xcel Energy,
the distribution utility serving Denver, this yields a residential demand density of 1,249
kW per km-sq. Residential customers make up 32 percent of Xcel Energy's Colorado retail
electricity sales (U.S. Energy Information Administration, 2012), so the residential
demand density is scaled up to account for non-residential loads, leading to a total load
power density of 3,890 kW per km-sq. This total load density is then allocated across
voltage levels as follows: 32 percent low-voltage (LV), 45 percent medium-voltage (MV),
and 22 percent high-voltage (HV).
Table 2-2: Load power parameters for simulated network
Unit
LV
MV
HV
kW/km-sq
1,249
1,765
876
kW
6
100
1,000
Load average power factor
p.u.
1
1
1
Load StDev power
p.u.
0.28
0.2
0.2
Load StDev energy
p.u.
0.28
0.2
0.2
Load StDev PF
p.u.
0.1
0.125
0.2
Parameter
Load power density
Load point average power
Note that an estimated 7 percent of Xcel Energy's Colorado customers reside in the 120
km-sq region of Denver encompassed by this simulated network.3 The estimated power
density derived above yields a total peak power demand of 468,079 kW in the simulated
4
network, which is also 7 percent of Xcel's actual total summer peak demand, verifying
that this method yields a realistic power demand density.
Both the average peak power and power factor for each individual load point in the
simulation is determined by random sampling from a truncated normal distribution with
mean and standard deviation specified for each voltage level as in Table 2-2. The average
power for each load point are set to be representative of typical LV, MV, and HV
1 Xcel Energy operates in Colorado as Public Service Company of Colorado. Xcel's Colorado residential
customers consume an average of 659 kWh during the summer months (Colorado Department of
Regulatory Agencies, 2014), for an average power demand of 0.92 kW. At a load factor of 0.5, consistent
with the average of residential load profiles used in this simulation, that yields an average peak residential
demand of 1.84 kW.
2 Data was unavailable on the actual allocation of power density at each voltage level for Xcel Energy's
service territory. These values were selected to match the share of residential, commercial, and industrial
loads in Xcel's Colorado territory (U.S. Energy Information Administration, 2012), using residential loads as
a proxy for LV customers, commercial loads as a proxy for MV customers, and industrial loads as a proxy
for HV customers.
3 Xcel Energy has 1,150,181 residential customers in Colorado (Navigant Consulting, 2010). The simulated
network spans 120 sq-km at a population density of 1,580.5 persons/km-sq, for an estimated population
of 190,190 persons. Assuming 2.3 persons per habitation, that yields 81,745 residential electricity
customers in the simulated network, or 7.1 percent of Xcel's total Colorado residential customers.
4 Xcel's 2010 Colorado summer peak demand was 6,262,000 kW (Navigant Consulting, 2010).
36
Jesse D. Jenkins (2014)
Chapter 2: Constructing a Simulated Electricity Distribution Network
customers in Xcel's service territory.5 The average power factor for each load point is set
to 1.0. Peak power is assumed to vary more significantly for lower voltages, while power
factor varies more significantly at higher voltages. A minimum peak power of 1 kW is
specified for each load point to prevent unrealistically small loads at the far "left tail" of
the distribution.
2.2 - Assigning Load Profiles
Three types of loads are considered by this simulation: residential, commercial, and
industrial loads. Load points are divided among these customer types based on the shares
specified for each voltage level in Table 2-3, and each load point is assigned one of ten
different 48-hour load profiles for each customer type. The shares of loads by customer
type were selected to ensure that the share of total annual electricity consumption for
industrial, commercial, and residential consumers in the simulated network closely
matches the real distribution of retail electricity sales in Xcel's Colorado service territory.6
Table 2-3: Distribution of load profiles in simulated network by load type
Parameter
Unit
LV
MV
HV
Industrial profile share
p.u.
0
0.1
0.4
Commercial profile share
p.u.
0.25
0.6
0.6
Residential profile share
p.u.
0.75
0.3
0
Load profiles for residential and commercial customers are derived from simulated hourby-hour annual load profile data from Department of Energy (DOE) reference building
models and correspond to TMY3 meteorological database characteristics for Denver
(National Renewable Energy Laboratory, 2013), yielding realistic load profiles specific to
the simulation area.7 As the DOE dataset does not include industrial load profiles, ten
different load profiles are created to approximate industrial loads. 8 The 48 hour profiles
selected for the simulation correspond to two non-consecutive days in the annual DOE
s Xcel segments commercial and industrial customers into three classes based on peak contracted
demand: less than 25 kW; 25-200 kW; and greater than 200 kW. This thesis assumes these values
correspond to LV, MV, and HV connections.
6The share of total electricity sales by customer class for Xcel's Colorado territory is as follows: 32 percent
residential; 45 percent commercial; 22 percent industrial (U.S. Energy Information Administration, 2012).
7 The DOE reference building load profile database contains four residential building load profiles. Six
additional residential load profiles are created by altering these base load profiles to yield a total of ten
different residential profiles. Ten commercial load profiles are selected from the 16 available commercial
profiles in the DOE dataset.
8 Since industrial load profiles are very dependent of the particular process they supply, a nearly-constant
consumption with some hourly variability can be assumed. Industrial profiles are therefore constructed as
a random walk around a base demand level.
37
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
dataset selected to match (1) the day of peak net power withdrawal and (2) the day of
peak power injection assuming penetration of photovoltaic (PV) generators in the
network.9 These two days capture the extremes in power flow to which the distribution
network must be designed (see Figures 2-1, 2-2, and 2-3).
Figure 2-1: Industrial load profiles
Peak Power Withdrawal Day
Peak Power Injection Day
1.2
1
0.8
0.6
-Industrial
1
-industrial
2
-Industrial
3
-Industrial
4
-Industrial 5
0,4
-Industrial
6
-Industrial
7
-industrial
8
Industrial 9
0,2
-Industrial
10
...Average
0
1 2 3 4 5 6 7 8 9 10111213141516171819 20 212223 24 25 26 27 28 29 30 3132 33 34 35 36 37 38 39 40 4142 43 44 45 46 47 48
Hour
Hour
Figure 2-2: Commercial load profiles
Peak Power Withdrawal Day
Peak Power Injection Day
1.2
0.8
S0.6
K
-Commercial
1
-Commercial
2
-Commercial
3
-Commercial
4
Commercial 5
**.
0.4
Commercial 6
-Commercial
7
Commercial a
X'..
Commercial 9
0.2
-Commercial
---
10
Average
0
1 2 3 4 5 6 7 8 9 101112131415161718192021222324
Hour
252627282930313233343536373839404142434445464748
Hour
9 Given the DOE reference building loads and annual PV production data Denver, peak net power demand
occurs at 18:00 hours on July
2 6 th
and peak reverse power flow occurs at 13:00 hours on March
38
1 1
th.
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
Figure 2-3: Residential load profiles
Peak Power Withdrawal Day
Peak Power Injection Day
1.2
0.8
I
-Residential
2
-
Residential 3
0.6
0.4
-Residential
M,
X.
-.-
-Residential
4
-Residential
5
-
Residential
-Residential
6
7
-Residential B
Residential 9
-- Residential 10
*,Average
0
1 2 3 4 5 6 7 8 9 101112131415161718192021222324 25 26 27 28 29 30 3132 33 34 35 36 37 38 3940414243 44454647 48
Hour
Hour
2.3 - Determining Network Topology and Assigning Geographic Location of Loads
The simulation method ensures a realistic network topography by using real street maps
for a 120 square kilometer portion of Denver (Figure 2-4) as a "scaffold" to constrain the
location of network users. The street map is first scanned and the layout of streets is
recognized (Figure 2-5). The layout of streets is then used as a proxy for the density of
network connection points by randomly assigning each load point to a specific geographic
coordinate along one of the recognized streets with an equal probability per unit of street
length (Figure 2-6). Load points are assigned a location using random sampling without
replacement. The location of the primary transmission interconnection substation is
assigned as per specification in the configuration files.
39
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
Figure 2-4: Street map of simulated distribution network area (Denver, Colorado)
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Jesse D. Jenkins (2014)
Chapter 2: Constructing a Simulated Electricity Distribution Network
Figure 2-5: Layout of streets is recognized creating "scaffold" for network topology
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Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
Figure 2-6: Network users are assigned along street map scaffold
LV customers shown as small black dots; MV customers medium-sized blue dots; HV customers larger
orange dots; primary transmission substation shown as green triangle.
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Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
2.4 - Constructing the Network with the Reference Network Model
After the location and profiles of load points are determined, a reference network model
(RNM) is used to construct the base distribution network. A description of the model can
be found in Domingo et al. (2011). This model was developed by Comillas University in
collaboration with the Spanish national regulator to calculate allowed remuneration of
electricity distribution companies, and it has been applied to research the impact on
distribution networks of large-scale deployment of DG, active network management, and
electric vehicle penetration (Cossent et al., 2011; Fernandez et al., 2011; Olmos et al.,
2009).
The RNM emulates the engineering design process of an electric distribution company by
specifying the placement and layout of all major distribution network components
connecting one or more primary transmission interconnection substations with all power
injection or consumption points (i.e., loads and DG). The network is constructed to
minimize total network costs (including capital expenditures, operational expenditures,
and ohmic network losses) while meeting three specified quality of service constraints:
(1) maximum system average interruption duration index (SAIDI); (2) maximum system
average interruption frequency index (SAIFI); and (3) maximum acceptable voltage range
at every node.
The RNM is able to run in two modes: a "greenfield" mode and a "brownfield" or
expansion-planning mode.
The greenfield mode builds an efficient network from scratch using the location and
maximum contracted power flow of each network user and each transmission substation
in conjunction with simultaneity factors to size network components. The simultaneity
factors for each network component specify what portion of total power flow
downstream of the component contributes to peak power flow for that component and
capture the heterogeneity of network users (i.e., not all load points peak at the same
hour). See Domingo et al. (2011) for more on these factors. The placement of these
simultaneity factors are depicted in Figure 2-7.
43
Jesse D. Jenkins (2014)
Chapter 2: Constructing a Simulated Electricity Distribution Network
Figure 2-7: Typical distribution network structure with simultaneity
factors represented as a bold point (Source: Domingo et al., 2011)
Type of faclities
Network structure and
simultaneity factors
610)kW
HV
MV
LV
(1,36)kV
MV
<1 kW
LV
W
I Customer
I
k U-Stonler
wtomin
Transformcr + Simultaneity
factor
--->Network
factor
+ Simultaneity
<::=>Network
Transformer
J
I
The brownfield mode takes an established network layout as an input and determines
additional network components and reinforcements necessary to accommodate changes
in network uses. The brownfield mode also takes into account 48-hour power
consumption and injection profiles for each network user (load or DG). To account for the
diversity embedded in the thirty load profiles used in the simulation, the simultaneity
factors used in the brownfield model runs are calculated by adjusting the factors used in
the greenfield runs upwards to ensure consistency between the two modes. 10
Table 2-4: Voltage levels and simultaneity factors used in the RNM model
Unit
LV
MV
HV
Voltage
kV
0.24
12
33/66
Simultaneity Factor
p.u.
0.30
0.80
1.0
Parameter
In both modes, the RNM builds out a network with four voltage levels (LV, MV, and HV-1
and HV-2) in a balanced three-phase configuration. Network components are selected
from a standard catalog file which contains technical and cost information about available
equipment and the cost and time burden of maintenance and power restoration actions.
The RNM also employs an algorithm that constrains the layout of network components
to align with the corresponding street map for the area (see Domingo et al., 2011). This
algorithm ensures a realistic layout of the network by avoiding placing overhead or
used in the
example, a simultaneity factor of 0.92 is already embedded in the mix of LV load profiles
is adjusted
run
Denver simulation. Thus, the LV customer simultaneity factor of 0.3 used in the Greenfield
brownfield
the
upwards by dividing by 0.92 to arrive at the simultaneity factor used for LV customers in
runs: 0.3 / 0.92 = 0.33.
10 For
44
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
underground distribution lines through the middle of city blocks and minimizing the
number of crossings of major avenues. Finally, the model takes into account the cost of
capital, lifetime of assets, discount rate, and the cost of losses.
To construct the simulated distribution network for Denver, this thesis first employs the
RNM in greenfield mode to build the base network layout. Next, the model is run again in
brownfield mode to ensure the network design accounts for the specific power profiles
at each load point. The layout generated by the greenfield run is used as the input for the
brownfield run which recalculates the optimal network design taking into account the
48-hour power profiles of network users.
The resulting base network is depicted in Figure 2-8. The total network investment cost
estimated by the RNM is $418.05 million (in overnight costs) as shown in Table 2-5. This
is the estimated replacement value of the network and is used in Chapter 3 to calculate
the regulated asset value (RAV) or "rate base" of the utility at the outset of the regulatory
period.
Table 2-5: Estimated efficient network costs for the simulated base network
New Network
Investment
Network
components
New Quality
Equipment
Total New
Network
Investment
Preventive
Maintenance
Corrective
Maintenance
Total
Maintenance
Annual costs (US$)
Overnight costs (US$)
LV feeders
$50,096,168
$0
$50,096,168
$792,654
$620,343
$1,412,997
LV/MV
transformers
$22,706,658
$0
$22,706,658
$1,332,445
$60,829
$1,393,274
MV feeders
$120,526,302
$18,746,320
$139,272,622
$703,912
$618,606
$1,322,517
MV/HV
substations
$147,384,000
$0
$147,384,000
$2,127,960
$589
$2128,549
$58,589,781
$0
$58,589,781
$211,176
$12,987
$224,163
$0
$0
$0
$0
$0
$0
$399,302,909
$18,746,320
$418,049,229
$5,168,147
$1,313,354
$6,481,501
HV lines
Transmission
substation
Total
45
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
Figure 2-8: The base simulated network for Denver, Colorado
LV lines in black; MV lines in blue; HV lines in red.
Insert: zoomed-in view of the portion of the network in vicinity of the primary transmission substation.
46
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
2.5 - Simulating Network Expansion Scenarios
Next, several network expansion scenarios are simulated for use in demonstrating the
proposed regulatory process in Chapter 3. These scenarios capture a range of possible
evolutions in network use, including variations in load growth and DER penetration. For
both load and PV, three forecasts are generated: a central forecast, and both high and
low sensitivity scenarios capturing the range of likely deviation from the central forecast
(see Table 2-6). Projected load growth is specified as both "vertical" load growth - i.e., a
percentage increase in demand at each load point during the regulatory period - and
"horizontal" load growth - i.e., a number of new load points at each voltage level
connected to the system during the regulatory period. Finally, to account for the impact
of DER penetration on distribution networks, the forecast includes projected penetration
of solar photovoltaic (PV) generators at each voltage level.
Table 2-6: Forecasted evolution of network uses (load growth and PV penetration)
Vertical load growth
(% increase from base system)
Approx. total load
LV
MV
HV
(million kWh/year)*
Base system
-
-
2,100.7
Low forecast
3.5
3.5
3.5
2,212.5
Central forecast
4.0
4.0
4.0
2,232.5
High forecast
4.5
4.5
4.5
2,252.5
Horizontal load growth
(# of new load points / kW peak)
LV
MV
HV
Approx. total peak
demand (kW)*
Base system
-
-
467,610
Low forecast
450 / 2,700
35 / 3,500
2 / 2,000
492,460
Central forecast
500 / 3,000
40 / 4,000
3 / 3,000
496,710
High forecast
550 / 3,300
45 / 4,500
4 / 4,000
500,980
*
Note: Total values include combined impact of both horizontal and vertical load growth
PV penetration
(# of new PV connections / kW peak)
LV
MV
-
Base system
-
Approx. total peak
generation (kW)
-
HV
Low forecast
1,875 / 22,500
270 / 54,000
5 / 10,000
86,500
Central forecast
2,083 / 25,000
300 / 60,000
6 / 12,000
97,000
High forecast
2,292 / 27,500
330 / 66,000
7 / 14,000
107,500
47
Jesse D. Jenkins (2014)
Chapter 2: Constructing a Simulated Electricity Distribution Network
The RNM is employed at this stage to estimate the efficient network costs across the
range of forecasted scenarios. There are nine cases covering all possible combinations of
the low, central, and high forecasts for load growth and PV penetration. For each case,
the RNM is run in brownfield mode, using the base network described in Section 2.4 as
input.
New load points are assigned to eligible connection points along streets using the same
random sampling without replacement method used to construct the base network (see
Section 2.3). New loads are assigned one of the thirty load profiles described in Section
2.2 with the same distribution of shares across customer types (industrial, commercial,
and residential) as in the base network. Power demand at each hour is increased at all
load points (existing and new) as per the appropriate vertical load growth in each case.
PV generators are assigned one of six PV production profiles shown in Figure 2-9. These
profiles are generated by the DOE's PVWatts solar PV production simulator (National
Renewable Energy Laboratory, 2014) and correspond to TMY3 meteorological database
characteristics for Denver, yielding realistic production profiles. From the annual hourly
production data produced by PVWatts, two non-consecutive 24-hour periods are selected
to match (1) the day of peak net power withdrawal (load minus PV production) and (2)
the day of maximum net power injection given the penetration of PV generators in the
network." These two days capture the extremes in power flow to which the distribution
network must be designed, given the particular combination of load profiles and PV
profiles used in this simulation. Six profiles are produced from the PVWatts calculator
corresponding to five possible fixed alignments of rooftop-mounted panels as well as one
single-axis tracking system. Individual generators are randomly assigned one of these six
Figure 2-91: Solar PV production profiles
Peak Power Injection Day
Peak Power Witidrawal Day
1.20
1.00
Fixed Roof, South
0-
Roof S
00-Fixed
Fixed Roof SE
Fixed Roof W
-Fixed Roof E
-
*.W
I-Axis
Tracking
... Average
.....-
0.20
0.00--1 2 3 4 5 6 7 8 9 101112131415161718192021222324
Hour
252627282930313233343536373839404142434445464748
Hour
Given the DOE reference building loads and annual Pv production data Denver, peak net power
demand occurs at 18:00 hours on July 2 6th and peak reverse power flow occurs at 13:00 hours on
March 1 1 th
1
48
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
profiles with a probability of 0.5 for a south-facing roof-mounted system (making this the
most common alignment) and 0.1 for each of the other five profiles.
The location of PV generators connected to LV and MV feeders is determined by matching
the generators with an existing load point using a placement algorithm which attempts
to minimize the difference between the annual electricity generation of the PV system
and the annual electricity consumption of the load point (both measured in kWh/year).
This placement algorithm is meant to mimic customer placement decisions under the
practice of "net metering," wherein customers with PV systems receive a credit on their
electricity bill for each kWh of electricity generated by their PV system, generally with a
limit set such that the total credits cannot exceed the customer's total electricity
consumption. Large PV systems connected to the HV sub-transmission network are
randomly placed at one of several pre-defined locations meant to designate likely
connection points for such large systems.
Note that the impact of PV systems on network costs is related to their generation profile,
their location in the network, and the consumption profiles of load points in the vicinity
of the generator (Vergara et al., 2014). Regulators should therefore take care to model
realistic placement decisions in this stage of the regulatory process. They may wish to
seek input from the utility and other stakeholders on the likely geographic pattern of the
penetration of PV and other DERs during the regulatory period (as part of Steps 1-5,
related to the forecast, discussed above).
To ensure consistency across cases, the set of new load points and PV generators (and
their locations and load profiles) used in the high forecast cases contains the full set of
loads and PV generators in the central forecast cases, and the set in the central forecasts
cases includes the full set of loads and generators in the low forecast cases. That is, for
each load Ii c Liow c Lcentrai c Lhigh and 4/c Lcentral c Lhigh and for each PV generator
g c Giow c Gcentrai c Ghigh and gj c Gcentrai c Ghigh.
Figure 2-10 depicts the location of new network users (loads and PV generators) in the
central forecast scenario as well as the network additions necessary to accommodate
these new network users, as determined by the RNM. Note that additional
reinforcements to existing network lines and protection equipment are also necessary,
but are not depicted in Figure 2-10.
49
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
Figure 2-10: Location of new network users and necessary network additions in central forecast
Existing network and load points in black; new load points in dark blue; new PV generators in light blue;
network additions to accommodate new network users in red.
Insert: zoomed-in view of a portion of the network
50
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
Table 2-7 summarizes the efficient network expenditures estimated by the RNM. New
network investments necessary to accommodate forecasted changes in network use
(load growth and PV penetration) are divided into primary network investments and
quality-related equipment (protection devices, voltage regulators, etc.) chosen by the
RNM to optimize quality of service. This yields the total incremental network investment
(expressed as overnight capital costs) required over the regulatory period. Annual
operations and maintenance expenditures are also estimated by the RNM and divided
into preventative maintenance and corrective maintenance costs. These values are
nominal annual costs and include both maintenance of the existing network and new
maintenance expenditures necessary to accommodate changes in network use over the
regulatory period.
Table 2-7: Estimated efficient network costs in central forecast scenario
Total New
New Network
Investment
Network
components
New Quality
Equipment
Network
Investment
Preventive
Maintenance
Overnight costs (US$)
Corrective
Total
Maintenance Maintenance
Annual costs (US$)
LV feeders
$1,625,755
$0
$1,625,755
$814,148
$637,103
$1,451,251
transformers
$2,293,146
$0
$2,293,146
$1,467 023
$66,973
$1,533,996
MV feeders
$1,178,007
$74,100
$1,252,107
$709,886
$623,117
$1,333,003
substations
$0
$0
$0
$2,127,960
$589
$2,128,549
$7,391,355
$0
$7,391,355
$237,752
$14,621
$252,373
$0
$0
$0
$0
$0
$0
$12,488,262
$74,100
$12,562,362
$5,356,768
$1,342,403
$6,699,171
HV lines
Transmission
substation
Total
51
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
References
Colorado Department of Regulatory Agencies. (2014). About Xcel Energy's Seasonal
Tiered Rates. Retrieved April 26, 2014, from
http://cdn.colorado.gov/cs/Satellite/DORA-PUC/CBON/DORA/1251642963374
Cossent, R., Olmos, L., G6mez, T., Mateo, C., & Frias, P. (2011). Mitigating the Impact of
Distributed Generation Advanced Response Options. International Transactions on
Electrical Energy Systems, 21(6), 1869-1888. doi:10.1002/etep.503
Domingo, C. M., G6mez, T., S nchez-Miralles, A., Peco, J., & Martinez, A. C. (2011). A
Reference Network Model for Large-Scale Street Map Generation. IEEE
Transactions on Power Systems, 26(1), 190-197.
Fernandez, L. P., G6mez, T., Cossent, R., Domingo, C. M., & Fri'as, P. (2011). Assessment
of the Impact of Plug-in Electric Vehicles on Distribution Networks. IEEE
Transactions on Power Systems, 26(1), 206-213.
National Renewable Energy Laboratory. (2013). Commercial and Residential Hourly Load
Profiles for all TMY3 Locations in the United States. Retrieved April 26, 2014, from
http://en.openei.org/datasets/node/961
National Renewable Energy Laboratory. (2014). PVWatts Calculator (March 2014 Beta
Release). Retrieved from http://pvwatts.nrel.gov/
Navigant Consulting. (2010). 2010 Colorado Utilities Report: A Report to the Colorado
Governor's Energy Office.
Olmos, L., Cossent, R., G6mez, T., Mateo, C., Joode, J. De, Scheepers, M., Gerhardt, N.
(2009). Case studies of system costs of distribution areas (No. WP 4, Deliverable 5).
Petten, the Netherlands. Retrieved from
http://www.improgres.org/fileadmin/improgres/user/docs/D5_casestudiesof-sy
stem costs.pdf
U.S. Census Bureau. (2011). Table 1. Annual Resident Population Estimates, Estimated
Components of Resident Population Change, and Rates of the Components of
Resident Population Change for States and Counties: April 1, 2010 to July 1, 2011.
2011 Population Estimates. Retrieved April 26, 2014, from
http://www.census.gov/popest/data/counties/totals/2011/files/CO-EST2011Alldata.csv
U.S. Census Bureau. (2014). Denver, Colorado: People QuickFacts. State & County
QuickFacts.
52
Chapter 2: Constructing a Simulated Electricity Distribution Network
Jesse D. Jenkins (2014)
U.S. Energy Information Administration. (2012). Colorado Electricity Profile 2010 - Table
3. Top Five Retailers of Electricity, with End Use Sectors. State Electricity Profiles.
Vergara, C., Perez-Arriaga, I., Mateo, C., & Frias, P. (2014). Estimating the Aggregate
Impact of Distributed Photovoltaic Generation over Distribution Networks (No. lIT14-034A). Madrid.
53
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D.
Jenkins (2014)
3 - Demonstrating the Proposed Regulatory Process
Using the simulated distribution network described in Chapter 2, this chapter
demonstrates in detail the application of the novel regulatory process for the
remuneration of electricity distribution utilities introduced in Section 1.3 and summarized
in Figure 3-1. The proposed regulatory process involves ex ante calculation of allowed
revenues and establishment of clear rules for annual ex post evaluations of actual
expenditures and adjustments to final allowed revenues. This process provides strong
incentives for the regulated utility to implement cost saving efficiencies, equalizes
efficiency incentives across operational and capital expenditures (OPEX and CAPEX), and
mitigates the impacts of uncertainty on both ratepayers and the regulated firm. In
addition, the proposed methods equip the regulator with powerful tools to overcome
their informational disadvantages vis-&-vis the regulated firm and minimizes incentives
for the firm to engage in strategic behavior during the regulatory process.
3.1 - Forecasting the Evolution of Network Uses
The ex ante regulatory process begins with the utility submitting to the regulator a
detailed year-by-year forecast of the evolution of network uses over the upcoming
regulatory period (Step 1).1 This forecast should at minimum include a set of appropriately
justified scenarios covering a range of the likely load and DER penetration levels, including
discussion of the most likely geographic evolution of loads and DERs. The regulator then
critically reviews this forecast (Step 2). This review may also include a period of open
comment on the preliminary forecast by stakeholders. At the conclusion of this review,
the regulator will submit clear comments to the utility on required changes or further
analysis needed to construct a final forecast (Step 3). Upon receiving this feedback, the
utility will then perform any required updates to their scenarios (Step 4) and re-submit a
final forecast to the regulator for use throughout the remainder of the regulatory process
(Step 5).
While in practice, this forecast would be created through several important, iterative
steps involving the utility, regulator, and key stakeholders, this demonstration of this
regulatory method begins by directly constructing a final forecast, meant to represent the
end-product of Steps 1-5.
1 This general method is agnostic as to the length of the regulatory period, although this length should be
carefully determined to balance the appropriate incentives for cost savings on the one hand and
adjustments to mitigate uncertainty on the other hand. In general, the profit and risk sharing mechanism
established by the proposed menu of contracts along with use of adjustment factors should reduce the
impacts of uncertainty and enable a longer regulatory period than would otherwise be feasible. To
demonstrate this process herein, a five-year regulatory period is selected.
54
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
Figure 3-1: Proposed regulatory process for the remuneration of electricity distribution utilities
Ex ante process
(performed only at beginning of regulatory
Regulator
1.
Submission of utility's forecast for evolution of networK uses
2.
Analysis of utility forecast / stakeholder comments
3.
Regulator's comments on utility forecast
4.
Revisions to utility forecast
5.
Final forecast for evolution of network uses
6.
Establishment of regulator's ex ante TOTEX estimate
7.
Construction of incentive compatible menu of contracts
8.
Submission of utility investment plan and estimated TOTEX
9.
Calculation of ex ante TOTEX and revenue baselines and ex
post sharing factor
Utility
IL
TI
10. Calculation of automatic adjustment factors ("delta factors")
to accommodate uncertainty in forecasted network use
11. Final regulatory contract published
Ex post process
(performed at the end of each year)
Regulator
I
1.
Utility makes expenditures and reports to regulator
2.
Audit of TOTEX and automatic adjustments to TOTEX baseline
computed due to deviation from forecasted network use
3.
Efficiency incentive computed as per menu of contracts and
adjustments made to allowed revenues
4.
Forward-looking revenue adjustments computed to true up
revenues
5.
Adjusted revenue allowance published
55
Utility
I
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
The forecast specifies three scenarios for the evolution of network uses: a central
forecast, and both high and low sensitivity scenarios capturing the range of likely
deviation from the central forecast (see Table 3-1). Projected load growth is specified as
both "vertical" load growth - i.e., a percentage increase in demand at each load point
during the regulatory period - and "horizontal" load growth - i.e., a number of new load
points at each voltage level connected to the system during the regulatory period. Finally,
to account for the impact of DER penetration on distribution networks, the forecast
includes projected penetration of solar photovoltaic (PV) generators at each voltage level.
While a real forecast would involve expected changes in load profiles beyond a simple
increase in average demand (vertical load growth) and would encompass a variety of new
DER network users, this simplified forecast will suffice to demonstrate the basic
application of the regulatory process.
Table 3-1: Forecasted evolution of network uses (load growth and PV penetration)
Vertical load growth
(% increase from base system)
Approx. total load
(million kWh/year)*
LV
MV
HV
Base system
-
-
-
2,100.7
Low forecast
3.5
3.5
3.5
2,212.5
Central forecast
4.0
4.0
4.0
2,232.5
High forecast
4.5
4.5
4.5
2,252.5
Horizontal load growth
(# of new load points / kW peak)
LV
MV
HV
Approx. total peak
demand (kW)*
Base system
-
-
-
467,610
Low forecast
450 / 2,700
35 / 3,500
Central forecast
500 / 3,000
40 / 4,000
2 / 2,000
3 / 3,000
492,460
496,710
High forecast
550 / 3,300
45 / 4,500
4 / 4,000
500,980
*
Note: Total values include combined impact of both horizontal and vertical load growth
PV penetration
(# of new PV connections / kW peak)
LV
MV
-
HV
-
Approx. total peak
generation (kW)
-
Low forecast
Central forecast
1,875 / 22,500
270 / 54,000
5 / 10,000
86,500
2,083 / 25,000
300 / 60,000
6 / 12,000
97,000
High forecast
2,292 / 27,500
330 / 66,000
7 / 14,000
107,500
Base system
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Chapter 3: Demonstrating the Proposed Regulatory Process
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It is important to note that the utility may have an incentive to engage in strategic
behavior during the construction of this forecast (Cossent & G6mez, 2013; Ofgem,
2010b). The utility may believe that inflating estimates of load growth or DER penetration
will lead to an increase in ex ante allowed revenues. This is an important concern, but is
mitigated through a variety of means. First, critical review of the preliminary forecast by
the regulator and comment by stakeholders provides an opportunity to illuminate any
strategic inflation in expected cost drivers. Second, use of automatic adjustment factors
(see Section 3.4) to account for departures from the final forecast over the regulatory
period for each key cost driver minimizes the incentive to engage in strategic behavior: if
forecasted load growth is inflated and realized load is much lower, for example, the ex
post automatic adjustment accounting for departures of load from the final forecast will
reduce final allowed revenues accordingly.
Note that constraining the utility's submission at this stage to only the forecast of network
uses, as opposed to a full business plan including estimated expenditures (as in the
process used by Ofgem, 2010 and 2013b or proposed by Cossent & G6mez, 2013) is
intended to minimize opportunities for the firm to engage in strategic behavior overall.
To fully ensure the theoretical incentive-compatibility of the menu of contracts designed
in Step 7 (see Section 3.3), the regulator's estimate of efficient network costs should be
set independently of the utility's cost estimate (see Ofgem, 2010, page 67). If the utility
submits its cost estimate in advance of the regulator developing its own estimate, the
firm may have an opportunity to influence the regulator. To avoid violating the firm
participation constraint and ensure the financeability of the utility, the regulatory has an
incentive not to produce a cost estimate too far below that of the utility. This may
introduce an incentive for the utility to artificially increase its reported estimate of
network costs that is not fully eliminated by the menu of contracts. The process proposed
herein avoids this potential problem and preserves the full incentive-compatibility of the
menu of contracts.
The potential disadvantage of this approach is that the regulator at this stage lacks the
detailed information contained in the utility's full business plan, which may be useful in
parameterizing the reference network model used in Step 6. Indeed, Ofgem employs a
menu of contracts precisely to increase the quality of information contained in these
business plans (Ofgem, 2010b). In addition, this lack of information does increase the risk
that the regulator may develop a cost estimate far off from that of the firm, raising the
potential of leaving significant rents to the utility (if the estimate is too high) or risking the
financeability of the firm (if the estimate is too low). However, this risk can be mitigated
via the design of the menu of contracts itself: i.e., by setting a lower weight on the
regulator's own cost estimate and/or a lower profit-sharing factor.
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Chapter 3: Demonstrating the Proposed Regulatory Process
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Ultimately, an independent, confident regulator may decide, as Ofgem has, that the
additional information in the full business plan can be obtained without unduly
influencing the regulator's estimate of costs. Note however that without the use of an
RNM, as proposed herein, the regulator has no practical option other than to accept a full
business plan from the utility in advance of calculating their own TOTEX estimate. Ofgem,
for example, develops their estimate of efficient costs by reviewing the firm's business
plan (with the assistance of analytical models and consultants). Employing the RNM, in
contrast, requires the comparatively more limited data inputs contained in the forecast
of network use and frees the regulator to set an estimate of efficient expenditures
independently of the firm's estimate. This could be a potential advantage over the
method employed by Ofgem (2009, 2010b, 2013b). As such, this thesis begins with the
forecast alone, demonstrating the practical implementation of this alternative process.
3.2 - Establishment of Regulator's Ex Ante Estimate of Efficient Expenditures
Following construction of the forecasted evolution of network uses, the regulator
constructs their ex ante estimate of efficient total network expenditures (TOTEX) over the
regulatory period (Step 6). To construct this estimate, the regulator will employ a
reference network model (RNM), a large-scale distribution network planning tool capable
of producing an estimate of efficient expenditures to expand and maintain a network to
serve a specified set of network users at prescribed quality levels (i.e., maximum
statistical probability of network disruptions, voltage limits, etc.) and considering
incentives for reduction of network losses. The RNM described in Domingo et al. (2010)
will be used throughout this thesis to demonstrate application of this method, but any
suitably rigorous RNM could be employed.
Use of an RNM has several informational requirements in addition to the final forecast of
network uses for the regulatory period. In order to estimate investment needs as
accurately as possible, the network used as the starting point for the RNM should
correspond to the actual assets operated by each utility in order to take into account the
established layout of the network and sunk investments in network components (Cossent
& G6mez, 2013). Regulators must therefore require utilities to report information on their
existing networks in a standard format including: the location, voltage level, contracted
capacity, and injection/withdrawal profile of all existing network connections (loads and
DG); the layout, impedance, and capacity of the high-voltage (HV), medium-voltage (MV),
and low-voltage (LV) electrical lines and protection devices; and the capacity and location
of transmission interconnection substations, HV/MV substations and MV/LV
transformers. Along with the final forecast for the regulatory period, these details on
existing network layout are used as inputs for a "brownfield" or expansion-planning RNM
that plans the necessary network expansion and maintenance expenditures needed to
58
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
serve forecasted network uses over the regulatory period (Domingo, G6mez, SanchezMiralles, Peco, & Martinez, 2011).
Alternatively, the regulator can reduce the information reporting burden by running a
"greenfield" RNM model which builds an efficient reference network from scratch using
only the location and capacity of HV, MV, and LV network users (loads and DG) and the
transmission interconnection substations (Domingo et al., 2011). The outputs of the
greenfield RNM can then be used as inputs for the brownfield RNM used to model
network expansion to serve forecasted network uses over the regulatory period.
However, since the greenfield RNM builds a new simulated network from scratch, the
resulting network may differ from the real utility network due to idiosyncratic path
dependencies in the real network's prior development, reducing the accuracy of resulting
estimate of efficient network costs (Cossent & G6mez, 2013).2
In either case, the regulator must also maintain a detailed library of standard network
facilities and equipment for all voltage levels, including: cables, overhead lines,
distribution transformers, substation components, and protection devices. These items
are used by the model to plan necessary network investments and should adequately
characterize the real investment alternatives the utility may face. As such, this library
should be updated regularly to reflect the current cost of standard components and
expanded to include any new network components recently entering common use, such
as new "smart grid" related components (i.e. ICT equipment, advanced power electronics,
etc.). To avoid opportunities for strategic behavior via inflation of reported component
costs, the regulator should develop costs for library components by benchmarking
efficient unit costs across multiple utilities (Cossent & G6mez, 2013; Cossent, 2013).
Finally, the regulator should also specify several general economic and technical
parameters, including the duration of the planning horizon, the weighted average cost of
capital (WACC) for utility investments, the cost of energy losses, and appropriate quality
metrics such as voltage limits and continuity of supply requirements. See Domingo et al.
(2011) for a detailed discussion of input parameters for this particular RNM.
Clearly these information requirements are significant. However, similar requirements
have been successfully implemented in Spain, Chile, and Sweden, each of which employ
RNMs for benchmarking purposes in the remuneration process (Cossent, 2013; Domingo
et al., 2011; Jamasb & Pollitt, 2008). In Spain, utility companies are obligated by law to
An intermediate solution is also possible, wherein utilities submit data only on the location and capacity
of primary transmission substations, HV/MV substations, and MV/LV transformers along with the location
and capacity of loads and DG. The greenfield RNM can then be run using these locations as constraints,
forcing the layout of the network to more closely align to the utility's real network while reducing
information reporting requirements (i.e. information on the layout and characteristics of the feeders and
protection devices would not be necessary).
2
59
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
provide the regulator with necessary data, and similar requirements are likely necessary
for this benchmarking method to be adopted. At the same time, given the increasing
adoption of electronic equipment inventories and sophisticated geographic information
systems by electric utilities, the reporting requirements necessary for the regulator to
employ an RNM are likely to become an increasingly negligible hurdle over time.
If these information requirements can be met, an RNM equips the regulator with a
powerful tool to help overcome information asymmetries by providing a realistic
engineering-based benchmark for efficient network expenditures. Unlike statistical
benchmarking methods which rely on assessment of past expenditures, RNMs can be
forward looking and thus better capable of capturing the evolving nature of distribution
network uses. RNMs have already been applied to assess the impact on distribution costs
due to large-scale deployment of DG, active network management, and electric vehicle
penetration, for example (Cossent, et al., 2011; Fernandez et al., 2011; Olmos et al., 2009;
Vergara, et al., 2014). Additionally, as a reference network is constructed for each utility,
RNMs can capture the heterogeneity of utility networks, a particularly important feature
as DER penetration is likely to increase the heterogeneity between distribution networks
(see Section 1.2).
Table 3-2: Characteristics of simulated base network
Location
Denver, Colorado
Simulation area
Population density
Estimated population
Estimated load power density - Base network
Estimated peak power demand - Base network
120.3
1,580.5
190,187
3,890
468,079
km-sq
persons/km-sq
persons
kW/km-sq
kW
Load Points - Base Network
Industrial
Commercial
Residential
Total
LV
0
6,263
18,788
25,051
MV
212
1,274
637
2,123
HV
42
63
0
105
Total
254
7,600
19,425
27,279
For demonstration of this regulatory process, the realistic, large-scale urban distribution
network simulated in Chapter 2 will be used as the base network. This network
corresponds to a roughly 120 km-sq portion of Denver, Colorado, and is parameterized to
closely simulate realistic conditions (see Table 3-2), including geographic distribution of
network users using a real street map as a "scaffold" to constrain locations, use of thirty
realistic load profiles (ten each for industrial, commercial, and residential users), and a
specification of load power density and distribution of load points among user types
(industrial, commercial, and residential) and voltage levels that closely matches the real
60
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
distribution of retail electricity sales in Denver. See Chapter 2 for details on simulation
methods.
Next, using this base network as input, the RNM is run in brownfield mode to compute
the efficient network expenditures necessary to accommodate the central forecast for
the evolution of network uses as developed in Steps 1-5 above (see Table 3-1). The
location, size, and profiles of new load points and PV generators are specified using the
simulation methods detailed in Section 2.5. Table 3-3 summarizes the efficient network
expenditures estimated by the RNM for the central forecast.
Table 3-3: Estimated efficient network costs in central forecast scenario
Total New
New Network
Investment
Network
components
LV feeders
New Quality
Equipment
Network
Investment
Preventive
Maintenance
Overnight costs (US$)
Corrective
Maintenance
Total
Maintenance
Annual costs (US$)
$1,625,755
$0
$1,625,755
$814,148
$637,103
$1,451,251
transformers
$2,293,146
$0
$2,293,146
$1,467 023
$66,973
$1,533,996
MV feeders
$1,178,007
$74,100
$1,252,107
$709,886
$623,117
$1,333,003
$0
$0
$0
$2,127,960
$589
$2,128,549
$7,391,355
$0
$7,391,355
$237,752
$14,621
$252,373
$0
$0
$0
$0
$0
$0
$12,488,262
$74,100
$12,562,362
$5,356,768
$1,342,403
$6,699,171
LV/MV
MV/HV
substations
HV lines
Transmission
substation
Total
New network investments necessary to accommodate forecasted changes in network use
(load growth and PV penetration) are divided into primary network investments and
quality-related equipment (protection devices, voltage regulators, etc.) chosen by the
RNM to optimize quality of service. This yields the total incremental network investment
required over the regulatory period, expressed as overnight capital costs. Note that these
incremental network investments do not include any investments necessary to replace
existing network assets. Replacement investments are calculated separately below.
The RNM also estimates annual operations and maintenance expenditures, which are
divided into preventative maintenance and corrective maintenance costs. These values
include maintenance of the existing network and new maintenance expenditures
necessary to accommodate changes in network use over the regulatory period.
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Chapter 3: Demonstrating the Proposed Regulatory Process
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Note that the current implementation of the RNM used herein does not model active
system management or other novel approaches to reduce network costs in the face of
increased penetration of DER (see Cossent, et al., 2011; Eurelectric, 2013; Poudineh &
Jamasb, 2014; Trebolle, et al., 2010). This is appropriate for regulatory purposes at this
stage, as the regulator should establish economic incentives for these novel practices to
become commonplace. However, as these techniques become part of the utility's normal
repertoire, the RNM must be updated to ensure the efficient frontier estimated by the
model aligns with industry best-practices.
The efficient investment and annual maintenance expenditures estimated in Table 3-3
must then be converted to the regulator's ex ante estimate of total network expenditures
(TOTEX) for the regulatory period. Table 3-4 depicts these calculations.
First, the overnight cost of incremental investments is converted into an annual
investment schedule by dividing the annual overnight investment cost computed by the
RNM ($12.56 million, see Table 3-3) into even annual investments across the regulatory
period.3 These overnight cost figures are then adjusted for inflation to current year dollars
by applying the producer price index (PPI).
In addition to incremental investments to accommodate changes in network uses, some
portion of existing network assets reach the end of their useful life and must also be
replaced each year. The gross value of retiring assets would be obtained in reality from
audits of the firm's booked assets. This demonstration makes the simplifying assumption
that the gross asset value of booked network assets at the start of the regulatory period
corresponds to the replacement value of the base network simulated in Section 2.4
($418.05 million), and that the gross asset value is divided evenly into 40 annual vintages
of $10.45 million (assuming a 40 year average lifespan for network assets). The oldest
vintage in the gross asset base will be fully depreciated each year, and assuming the
financial life of assets corresponds to the physical useful life of these assets, this vintage
will need to be replaced with new or refurbished assets, to maintain the functionality of
the network.
This conversion uses a stochastic heuristic Markov-chain particle swarm robust optimization Monte
Carlos simulation method. For more, see: http://en.wikipedia.org/wiki/Easter ..egg_(media)
3
(Alternatively, one can divide the total sum by the number of years in the regulatory period...)
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Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
Table 3-4: Regulator's ex ante estimate of efficient total network expenditures
Assumes 5 year regulatory period and straight-line depreciation of RAV over 40 year average asset life;
base network assets are evenly divided among vintages for computation of average age of network assets
and gross asset value; pre-tax return on equity is 10%; cost of debt is 5.5%; gearing ratio is 35% equity,
65% debt and W.A.C.C. is 7.08%; inflation is 2.5%; discount rate is 6.5%; extended lifetime factor for
replacement investments is 0.67.4
Year 0
CAPEX (million $)
Incremental investment
Year 1
Year 2
Year 3
Year 4
Year 5
Annual cost (current year dollars)
NPV
Total overnight cost (incremental network cost from RNM):
Overnight cost
$2.51
$2.51
$2.51
$2.51
$2.51
Inflation adjusted
$2.64
$2.71
$2.77
$2.84
$2.58
Replacement investments
Total
Total gross asset value (base network cost from RNM):
$12.56
$11.22
$418.05
Overnight cost
$6.97
$6.97
$6.97
$6.97
$6.97
Inflation adjusted
$7.14
$7.32
$7.50
$7.69
$7.88
$31.10
$9.72
$9.96
$10.21
$10.46
$10.73
$42.32
Total investment (CAPEX)
OPEX (million $)
Network maintenance
Base value, no inflation
Annual cost (current year dollars)
$6.48
Inflation adjusted
TOTEX ESTIMATE (million $)
Total network expenditures
(TOTEX)
NPV
$6.52
$6.57
$6.61
$6.66
$6.70
-
$6.69
$6.85
$7.03
$7.20
$7.38
$29.13
$16.40
$16.81
$17.24
$17.67
$18.11
$71.40
59
59
59
59
59
59
41
41
41
41
41
41
FAST AND SLOW MONEY (% of TOTEX)
CAPEX share of TOTEX:
"Slow Money"
OPEX share of TOTEX:
"Fast Money"
To accommodate network replacement costs, regulators commonly allow the utility an
investment allowance equal to the full replacement value of the assets in the expiring
vintage (i.e. $10.45 million per vintage in this case). However, this method is likely to overcompensate the utility. Replacing an existing network asset will almost certainly cost less
than the original construction of that asset: trenches and rights of way for underground
and overhead cabling have already been dug, permits obtained, connections to other
assets installed, etc. In addition, existing assets can often be repaired and repurposed,
extending their useful life at lower cost than purchasing a new replacement asset.
Public Service Company of Colorado/Xcel's actual allowed pre-tax return on equity was set to 10% in 2012 (cross,
2012). Note that the implied after-tax cost of equity (6.5%) is very close to that calculated by Ofgem for RIO DPCR5
(6.7%) and the range used in ED-1 (6%-7.2%) (Ofgem, 2013, p. 15). Ofgem (2013, p. 12) reports a real cost of debt of
about 3%. This is added to the inflation rate to arrive at cost of debt in nominal terms. Inflation rate is based on the
producer price index for electric distribution utilities in the United States (U.S. Bureau of Labor Statistics, 2014). The
average PPI was 3.5% from 2004-2013 but only 1.9% from 2009-2013. A forward-looking value of 2.5% is used here.
The discount rate is aligned with the after-tax return on equity (6.5%).
4
63
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
Therefore, regulators should hire an independent auditor to assess the average
replacement cost as a percentage of the original asset costs. This percentage can be
considered the "extended lifetime factor" and should be applied to the full value of the
retiring asset vintage to obtain an estimate of efficient replacement costs that will avoid
over-compensating the utility.5 This demonstration assumes an extended lifetime factor
of 66.7%. Overnight replacement costs are then adjusted to current year costs by applying
the PPI.
Annual network maintenance costs (in non-inflation-adjusted terms) are estimated from
the RNM. The maintenance costs at the start of the regulatory period equal the expected
total maintenance costs for the base network simulated in Section 2.4, while the costs for
the final year of the regulatory period correspond to the expected total maintenance
costs for the expanded network that corresponds to the central forecast scenario (Table
3-3). Annual values for the interim years are imputed by assuming a compound annual
growth in maintenance costs over the regulatory period. These values are then adjusted
for inflation by applying the PP.
The regulator's estimate of efficient TOTEX is thus the sum of inflation adjusted CAPEX
(including incremental and replacement investments) and OPEX (network maintenance).6
In addition, the regulator can now determine the expected portion of TOTEX associated
with both CAPEX and OPEX. These shares are important if the regulator wishes to employ
a TOTEX-based approach to capitalizing expenditures into the regulated asset value (RAV)
used to determine allowed revenues (see Section 3.4 below).
If CAPEX and OPEX are treated separately by the regulator with CAPEX added to the RAV
and OPEX expensed fully during the regulatory period, this can distort incentives for the
utility and encourage over-investment (Ofgem, 2009). Both categories of incentives will
face the same efficiency incentives (assuming the incentives are applied to TOTEX, as
recommended herein), so a dollar of OPEX savings and a dollar in CAPEX savings will earn
the utility the same efficiency-related income (as determined by the sharing factor set by
the menu of contracts, see Section 3.3). However, if only CAPEX is capitalized into the
RAV, then that dollar in reduced CAPEX will also involve a reduction in the RAV, and thus
a reduction in the allowed return on equity and a corresponding decline in net profit for
shareholders. This decline in net profit will offset some portion of the efficiency-related
s This concept should be credited to Ignacio Perez-Arriaga.
6 Note that other business-related operational expenditures such as business support costs, pensions, etc.
are not included in this simulation and are thus excluded from OPEX figures here. These expenditures
would have to be accommodated in real revenue allowance determinations.
64
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
income, distorting tradeoffs between OPEX and CAPEX and potentially encouraging overinvestment.
This distortion can be very problematic given the increased need for active system
management approaches to accommodate DER at minimal cost (see Section 1.2). Active
system management strategies will involve greater operational expenditures, including
innovative contractual relationships with DER owners and aggregators, in an effort to
defer or offset network investments (Cossent, G6mez, & Olmos, 2011; Cossent, et al.,
2011; Fernandez et al., 2011; Perez-Arriaga, et al., 2013; Poudineh & Jamasb, 2014;
Trebolle, et al., 2010). Addressing this distortion and equalizing incentives for efficiency
across CAPEX and OPEX is thus paramount given the evolving nature of electricity
distribution systems (Ofgem, 2009, 2013b).
Recognizing this important distortion, Ofgem (2009) proposed a TOTEX-based approach
to capitalizing expenditures into the RAV which equalizes the incentives for cost-savings
across both CAPEX and OPEX. This approach was implemented in the Fifth Distribution
Price Control Review period (DPCR5, 2010-2015) and will be continued during the first
R1O price control period (RIIO ED-1, 2016-2024) (Ofgem, 2013b). Under this approach, all
realized network expenditures are treated in the same way by designating a fixed portion
of allowed TOTEX, referred to as "slow money," which is capitalized into the RAV (from
which depreciation and cost of capital allowances are calculated) with the remainder of
TOTEX designated as "fast money." The share of slow and fast money is fixed ex ante
based on the regulator's expected share of CAPEX and OPEX, respectively, in total
expenditures. The share of CAPEX and OPEX in actual utility expenditures during the
regulatory period is thus free to depart from this expected share without impacting the
utility's return on equity. Thus, cost-saving tradeoffs between both types of expenditure
can be fully exploited by the utility, and the balance of incentives is restored (see Ofgem,
2009, pages 117-120, and Ofgem, 2013a, pages 30-32 for more). The regulatory burden
is also reduced for both the utility and regulator, as neither party has to actively monitor
the boundaries between various categories of expenditures. This TOTEX-based approach
will be applied in this thesis, so the slow and fast money shares are computed and
reported in Table 3.4.
3.3 - Construction of an Incentive Compatible Menu of Contracts
Next, the regulator must construct an incentive compatible menu of profit-sharing
regulatory contracts for the utility (Step 7). A menu of contracts specifies an ex ante
regulatory allowance as well as a clear rule for ex post evaluation of actual expenditures
and adjustments to final remuneration. The menu outlines a continuum of profit-sharing
factors (sliding-scale efficiency incentives) wherein values depend on the ratio of the
65
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
utility's estimate of total network expenditures over the regulatory period to the
regulator's TOTEX estimate derived in Step 6 (Section 3.2). If the design of the menu
preserves incentive compatibility, it will provide incentives for the utility to provide a
truthful estimate of expected network costs, helping overcome information asymmetries.
In addition, incentives for the utility to engage in strategic behavior by inflating estimates
of network costs are eliminated.
Construction of the menu of contracts here follows the method set out in Cossent &
G6mez (2013). Additionally, this general approach has been successfully implemented by
Ofgem since the fourth distribution price control review (DPCR4, 2005-2010) and is now
an integral part of Ofgem's RIO framework.7
Using the method introduced in Cossent & G6mez (2013), the regulator only needs to
establish four discretionary regulatory parameters to create a continuous menu of
contracts:
1. The weight placed on the regulator's estimate of efficient network expenditures
relative to the utility's estimate, w. This weight should depend on how reliable the
regulator believes their estimate of future expenditures is likely to be relative to
the accuracy of the firm's estimate. A higher value places more weight on the
regulator's estimate, while a lower value places more weight on the firm's
estimate.
2. The reference value for the profit-sharing factor (the portion of cost
savings/increases to which the utility is exposed, also known as the efficiency
incentive rate), SFref, which corresponds to the case where the utility's estimate
of future expenditures aligns with the regulator's estimate (Rex ante = 1.0). This
value can be set to establish the strength of efficiency incentives faced by utilities
in order to manage tradeoffs between incentives for efficiency and rent extraction
taking into account the degree of uncertainty about future costs and demand. A
value of 1.0 corresponds to a pure revenue cap contract while a value of 0.0
corresponds to a cost of service contract. In general, under lower levels of
uncertainty, a higher profit-sharing factor (i.e., the firm is exposed to most of the
risks and rewards of cost savings) performs better, while a lower profit-sharing
factor (which shares most risks and rewards with ratepayers) performs better
under higher levels of uncertainty (Schmalensee, 1989). Figure 3-2 summarizes
7 The UK's menu of contracts approach, known as the Information Quality Incentive (IQI), is described in
Crouch (2006) as well in (Ofgem, 2009, 2010b, 2013c) and Cossent and G6mez (2013). See also Ofgem,
(2013a) for a financial calculations spreadsheet implementing the IQI.
66
Jesse D. Jenkins (2014)
Chapter 3: Demonstrating the Proposed Regulatory Process
several important considerations in setting the strength of the sharing factor (see
Ofgem, 2010, pp 84-87 for further discussion).
Figure 3-2: Regulatory considerations in establishing the sharing factor or incentive rate
(Source: Ofgem, 2010)
Company can operate with higher gearing
reducing cost of capital consumers need to fund
Stronger efficiency incentives
Reduce risk that company spends
Reduce risks that company faces financeability
problerms, potentially putting outputs at: risk
7-nsumers enjoy more of the unexpected cost
iangq achieved duning rne control rerind
frnonrey unnieLcess!arily jut to grow RAVl
bear smaller share of any
additional expenlit-ire incurred by
Consumer-s
I
Redceris
roittatorpanesmak wat
miyh beseenas
indal pofis
company durnng pnice :on,-1 penioc
I Reduce risk of distortions in expenditureI
iand cost
Reduce risk of distortions in expenditure from
one price control period to the next
I
ailocaition between regulated
III
d crpnes
business and unrat
same corporate group
3. The rate of change in the profit-sharing factor is the ratio between the utility's
estimate and the regulator's estimate changes, SFroc. This value can be set so as to
control the spread in efficiency incentives faced by different utilities during the
regulatory period. A larger value results in a wider range of profit-sharing factors
offered while a smaller factor results in a tighter range. Note that the regulator
may wish to set this value to ensure that the sharing factor does not fall below a
certain value (a lower bound of 0.3 for example). If the sharing factor is too low,
a company may not face enough exposure to the costs of overspending and could
face perverse incentives to increase their spending unnecessarily to increase their
regulated asset value (RAV) and allowed revenues (for more, see Ofgem, 2010, pp
85-86). As a rule of thumb, the sharing factor should not fall below the riskadjusted rate of return the utility would be expected to earn by increasing their
regulated asset value (i.e. their allowed returns on equity invested plus
depreciation adjusted for relative risk), and it may be advisable to ensure the
sharing factor stays a healthy margin above this rate.
4. The reference value for the additional income payment, Alref, used to ensure
incentive compatibility of the menu of contracts. This reference value corresponds
to the case where the utility's estimate of future costs aligns with the regulator's
estimate (Rexante = 1.0), and the selected value can be used to tune expected profit
margins for the utility.
Table 3-5 describes how each of the remaining initialization parameters are calculated
from these four discretionary parameters. The table also describes the formulas to
compute the appropriate ex ante regulatory contract and ex post efficient incentive, the
portion of realized over- or under-spend shared with the utility's shareholders.
67
Jesse D. Jenkins (2014)
Chapter 3: Demonstrating the Proposed Regulatory Process
Table 3-5: Parameters and formulas for construction of incentive compatible menu of contracts
Symbol
Formula/constraint
Description
DISCRETIONARY INITIALIZATION PARAMETERS
[0,1]
W
Weight on regulator's estimate [p.u.]
SFref
Reference value for sharing factor [p.u. share
of over/under-spend retained by firm]
SFroc
Rate of change of sharing factor with ratio
Alref
Reference value for additional income
[% of regulator's estimate]
<0
CALCULATED INITIALIZATION PARAMETERS
a
6
Intercept of additional income
Alref - 100 * SFref *(w - 1) + 104 *SFroc *(w - 0.5)
Alref - 100*a - 1002*6
1st order factor of additional income formula
SFref *(W - 1) + 100* SFroc *(1 - 2*w)
2nd order factor of additional income
formula
SFroc *(w - 0.5)
EX ANTE PARAMETERS
Xfirm
Firm's ex ante TOTEX estimate [$]
Submitted by firm
Xregulator
Regulator's ex ante TOTEX estimate [$]
Calculated by regulator using RNM
Ratio of firm's estimate to regulator's
estimate [%]
Xfirm / Xreguiator
baseline
Ex ante allowed TOTEX
[% of regulator's estimate]
W *100+(1 - w)*Rexante
SF
Sharing factor [p.u. share of over/under-
SFref + (Rexante - 100)* SFroc
Al
Additional income
[% of regulator's estimate]
AIint + a *Rex
Xex
ante
spend retained by firm]
ante - 6 *Rex ante2
EX POST PARAMETERS
Submitted by firm and audited by regulator
[$]
Xex post
Realized ex post TOTEX
Rex post
Ratio of realized ex post TOTEX to ex ante
allowed TOTEX baseline [%]
Xexpost/Xregulator
Ex post efficiency incentive [share of overElex post
(Xex
under-spend retained by firm as % of
onte -
Xex post)*SF / Xregulator
regulator's estimate]
Aex post
Final ex post adjustment to allowed revenues
[% of ex ante regulator's estimate]
Elex past +
Al
Tables 3-6 and 3-7 below present two examples of an incentive compatible menu of
contracts. The menu in Table 3-6 offers fairly high-powered incentives (SFref = 0.7;
SFroc = -0.01) and corresponds to a case where the regulator is fairly confident in their
estimates of efficient costs (w = 0.66). Table 3-7 represents a lower-powered set of
incentives (SFref = 0.4; SFroc = -0.005) and the regulator is less confident in their estimates
of efficient costs (w = 0.33). Both of these menus will be used to demonstrate the ex post
68
=
Jesse D. Jenkins (2014)
Chapter 3: Demonstrating the Proposed Regulatory Process
determination of revenues in Section 3.4 below. Note that while these tables show
discrete values in each column, a continuous menu of contracts can be calculated using
the formulas in Table 3.5.
Table 3-6: Example high powered menu of contracts
w = 0.66; SFref = 0.7 SFroc = -0.01; Alref = 1.0
Ratio of firm's cost estimate to
regulator's cost estimate [%]
Allowed revenues ex ante
[% of regulator's cost estimate]
Sharing factor [%]
Additional income
[% of regulator's cost estimate]
Ratio of realized ex post
expenditures to regulator's ex
ante estimate [%]
85
90
Rex ante
90
95
100
105
110
115
120
X,,, a,
96.6
98.3
100.0
101.7
103.4
105.1
106.8
SF
80.0
75.0
70.0
65.0
60.0
55.0
50.0
Al
3.2
2.2
1.0
-0.2
-1.5
-2.9
-4.4
Rex,,,s
Final ex post adjustment to allowed revenues [% of regulator's ex ante estimate]
8.1
9.5
10.6
11.5
12.1
12.5
5.4
6.5
7.4
8.0
8.4
95
4.5
100
105
110
115
120
125
0.5
-3.5
-7.5
-11.5
-15.5
-19.5
4.5
0.9
-2.9
-6.6
-10.4
-14.1
-17.9
Aexpost
6.5
4.0
4.1
3.5
2.6
1.5
0.9
0.5
-2.5
-0.1
-2.9
-5.6
-1.0
-3.5
-6.0
-8.5
-2.5
-6.0
-9.5
-13.0
-16.5
-5.6
-8.9
-12.1
-15.4
-8.5
-11.5
-14.5
-11.1
-13.9
-13.5
Table 3-7: Example low powered menu of contracts
w = 0.33 SFref = 0.4; SFroc = -0.005; Alref = 1.0
Ratio of firm's cost estimate to
regulator's cost estimate [%]
Allowed revenues ex ante
[% of regulator's cost estimate]
Sharing factor [%]
Additional income
[% of regulator's cost estimate]
Ratio of realized ex post
expenditures to regulator's ex
ante estimate [%]
85
90
95
100
105
110
115
120
125
Rexante
90
95
100
105
110
115
120
Xex ante
93.3
96.7
100.0
103.4
106.7
110.1
113.4
SF
45.0
42.5
40.0
37.5
35.0
32.5
30.0
Al
3.8
2.4
1.0
-0.3
-1.6
-2.8
-4.0
Re P,
Final expost adjustment to allowed revenues [% of regulator's ex ante estimate]
5.3
6.0
6.6
7.0
7.3
7.5
3.7
4.3
4.7
5.0
5.2
2.1
2.5
2.8
3.0
3.0
0.4
0.8
0.9
0.9
0.8
-1.2
-1.0
-1.0
-1.2
-1.5
-2.8
-2.8
-3.0
-3.3
-3.8
-4.5
-4.7
-5.0
-5.4
-6.0
-6.1
-6.3
-6.6
-7.0
-7.6
-8.3
-7.7
-8.0
-8.4
-9.0
-9.7
-10.5
Aexpjst
4.5
3.0
1.5
0.0
-1.5
-3.0
-4.5
-7.5
Both example menus illustrate incentive compatibility. Shaded cells correspond to the
cases for which the ex ante utility forecast matches actual expenditures. For any realized
value of network expenditures (i.e., any horizontal row in the bottom half of the matrix),
the utility will earn the greatest revenues in the case where their realized cost matches
their ex ante forecast. Efficiency incentives are also preserved, as lowering realized
69
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
expenditures below the utility's forecast (i.e., moving up in a vertical column) will increase
the utility's final revenues (and vice versa).
Employing an incentive compatible menu of contracts can help regulators address both
the heightened information asymmetry and uncertainty expected as distribution
networks evolve to accommodate new DERs and employ new smart grid capabilities (see
Section 1.2). First, the incentive compatible nature of the menu elicits accurate estimates
of expected costs from the utility and removes incentives to inflate estimated costs (i.e.,
engage in strategic behavior). Second, the combination of ex ante revenue determination
with clear ex post adjustment rules promotes efficient network investments while
minimizing the regulatory uncertainty that can deter investment.8 Finally, the
discretionary parameters used to construct the menu of contracts give the regulator
flexibility to tune the strength of incentives to mitigate the impacts of uncertainty. For
example, the weight placed on the regulator's estimate of network costs and the strength
of the efficiency incentive (the sharing factor) can be reduced in the face of greater
uncertainty or increased as the regulator becomes more confident in forecasts.
3.4 - Calculation of Ex Ante TOTEX and Revenue Baselines and Sharing Factor
With the regulator's estimate of efficient network expenditures and menu of contracts
on hand, the regulator can then assess the utility's estimate of network expenditures,
which is submitted as part of their detailed business plan in Step 8.
First, the utility's annual TOTEX estimates are compared to the regulator's estimates
produced in Step 6 and the ex ante TOTEX baseline for each year in the regulatory period
is established as per Equation 1:
(1)
Xex ante
=
Xregulator X (0 + Xfirm X
(1
-
w)
Where:
Xbaseline: annual total network expenditures baseline
Xregulator: regulator's
ex ante estimate of efficient total network expenditures
Xfirm: firm's exante estimate of efficient total network expenditures
o: weight placed on regulator's estimate
Next, the ratio between the total net present value (NPV) of the utility's TOTEX estimate
and the regulator's TOTEX estimate determines the sharing factor and additional income
allowances as defined by the menu of contracts produced in Step 7.
Regulatory certainty is further improved when this menu of contracts is combined with the automatic
adjustment factors discussed below.
8
70
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
Finally, the ex ante allowed revenue baseline for the regulatory period is calculated as per
Equations 2-8:
(2) Slow Money = Xex ante X Slow Money Share
(3) Gross Assetsy = Gross Assetsy_ 1 - Expiring Assets + Slow Moneyy
(4) RAV =
Lif e
*
Gross Assets
(5) Fast Money = Xex ante - Slow Money
(6) Depreciation=
(7)
Gross Assets
Lif e
Cost of Capitaly = RAVY_
1
x WACC
(8) Revenue Baseline = Fast Money + Depreciation+ Cost of Capital + Additional Income
Where:
Slow Money: notional CAPEX allowance (capitalized into RAV)
Slow Money Share: regulator's expected share of CAPEX in TOTEX
Gross Assets: total gross value of in-service assets
Expiring Assets: gross value of assets reaching end of useful life
y current year in the regulatory period
RAV: regulated asset value (gross value of assets less depreciation)
Life: regulatory life of assets
Age: average age of assets
Fast Money: notional OPEX allowance (expensed annually)
Depreciation:annual capital depreciation allowance
Cost of Capital:annual allowance for repayment of debt and equity
WACC: weighted average cost of capital
Additional Income: additional income allowance from menu of contracts
Revenue Baseline: exante allowed revenues for each year of the regulatory period
Note that the gross asset value and average age of assets in the base network at the
beginning of the regulatory period must be estimated for use in Equation 3 above. This
value would be obtained in reality from audits of the firms booked assets. For this
demonstration, a simplifying assumption is made: the total replacement value of the base
network simulated in Section 2.4 ($418.05 million) is used as the initial gross asset value,
while assets are divided equally and assigned to 40 annual vintages of $10.45 million. This
yields an average age of 19.5 years for network assets at the beginning of the regulatory
period. Each year, the oldest vintage of assets is fully depreciated, and thus removed from
the gross asset value, while new investments (both incremental and replacement
investments) made that year are added to the gross asset value as a new vintage. The
average age of assets is tracked each year, and expressed as a dollar-weighted average of
the age of each vintage in the gross asset base.
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Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
Table 3-8 computes an example revenue allowance for a case in which the utility's
estimate of TOTEX is higher than that of the regulator (ratio = 1.2) and using the higherpowered menu of contracts in Table 3-7.
Table 3-8: Example of TOTEX and revenue baseline calculations
w = 0.66; SFref = 0.7 SFroc = -0.01; Alref = 1.0; Slow Money Share = 59%; WACC = 7.09%; Avg asset life = 40yrs
1
Year 2
Year 3
Year4
Year 5
NPV
Regulator's estimate
$16.40
$16.81
$17.24
$17.67
$18.11
$71.44
Utility's estimate
$19.69
$20.18
$20.68
$21.20
$21.73
$85.73
$18.41
$18.87
$19.34
$76.30
Additional income
-$3.14
Year0
Year
TOTEX ESTIMATES
TOTEX BASELINE AND MENU OF CONTRACTS PARAMETERS
TOTEX baseline
$17.52
Ratio
1.2
$17.96
Sharing Factor
50%
REVENUE BASELINE CALCULATIONS
Capitalization
Slow money
$10.38
$10.64
$10.90
$11.18
$11.46
$418.05
$417.98
$418.16
$418.61
$419.34
$420.34
19.50
19.50
19.49
19.47
19.44
19.40
$214.25
$214.18
$214.36
$214.81
$215.52
$216.50
Fast money allowance
$7.14
$7.32
$7.50
$7.69
$7.88
$31.11
Depreciation allowance
$10.45
$10.45
$10.45
$10.47
$10.48
$43.47
Cost of capital allowance
$15.16
$15.15
$15.17
$15.20
$15.25
$63.09
Additional income
-$0.72
-$0.74
-$0.76
-$0.78
-$0.80
-$3.14
$32.03
$32.18
$32.37
$32.58
$32.82
$134.5
Gross asset value
Average age of assets (yrs)
Regulated asset value
Cost allowances
Revenue baseline
Together, the ex ante TOTEX and revenue baselines and the ex post sharing factor define
the contract between the regulator and the utility for the duration of the regulatory
period. This regulatory contract provides the utility with a clear expectation of how their
revenues will evolve over the regulatory period and provides clear incentives for efficient
management of network costs.
Tables 3-9 through 3-11 illustrate the regulatory contracts associated with three different
cases: one where the utility and regulator's ex ante cost estimates align (ratio = 1.0), one
where the utility estimates a higher cost than the regulator (ratio = 1.2), and one where
the utility estimates a lower cost than the regulator (ratio = 0.9). For each case, the
regulatory contract describing the ex ante remuneration rules for the utility is described
for two menus of contracts: one with a higher powered efficiency incentive (Table 3-6)
and the other with a lower-powered incentive (Table 3-7). These cases illustrate a range
72
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
of possible regulatory contracts and demonstrate the flexibility the regulator has in
specifying the parameters for constructing the menu of contracts.
Table 3-9: Example of ex ante remuneration rules: utility and regulator estimates align
Slow money share = 59%; WACC = 7.09%; Avg asset life = 40yrs.
Year 1
Year 2
Year 3
Year 4
Year 5
NPV
Regulator's cost estimate
$16.40
$16.81
$17.24
$17.67
$18.11
$71.44
Utility's cost estimate
$16.40
$16.81
$17.24
$17.67
$18.11
$71.44
$17.67
$18.11
$71.44
$32.85
$33.04
$135.90
$17.67
$18.11
$71.44
$32.85
$33.04
$135.90
Year 0
CASE 1: RATIO 1.0
Higher powered contract: w = 0.66; SFref = 0.7 SFroc = -0.01; Alref = 1.0
$16.40
TOTEX baseline
70%
Sharing factor
$16.81
Additional income
$0.71
$32.56
$32.69
$32.46
Revenue baseline
$17.24
Lower powered contract: w = 0.33 SFref = 0.4; SFroc = -0.005; Alref = 1.0
$16.81
$16.40
TOTEX baseline
70%
Sharing factor
Revenue baseline
$17.24
Additional income
$0.71
$32.46
$32.69
$32.56
Table 3-10: Example of ex ante remuneration rules: utility forecasts higher cost than regulator
Year1
Year2
Year3
Year4
Year5
NPV
Regulator's cost estimate
$16.40
$16.81
$17.24
$17.67
$18.11
$71.44
Utility's cost estimate
$19.69
$20.18
$20.68
$21.20
$21.73
$85.73
$18.87
$19.34
$76.30
$32.58
$32.82
$134.52
$20.03
$20.53
$81.02
$33.31
$33.62
$137.20
Year0
CASE 2: RATIO 1.2
Higher powered contract: w = 0.66; SFref = 0.7 SFroc = -0.01; Alref = 1.0
Sharing factor
$17.96
$18.41
Additional income
-$3.14
$32.18
$32.37
$17.52
TOTEX baseline
50%
$32.03
Revenue baseline
Lower powered contract: w = 0.33 SFref = 0.4; SFroc = -0.005; Alref = 1.0
Sharing factor
Revenue baseline
$19.07
$19.54
Additional income
-$2.87
$32.53
$33.02
$18.60
TOTEX baseline
30%
$32.76
73
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
Table 3-11: Example of ex ante remuneration rules: utility forecasts lower cost than regulator
Year 0
Year 1
Year 2
Year 3
Year 4
Year 5
NPV
Regulator's cost estimate
$16.40
$16.81
$17.24
$17.67
$18.11
$71.44
Utility's cost estimate
$14.76
$15.13
$15.51
$15.90
$16.30
$64.30
$17.07
$17.49
$69.02
$32.90
$33.06
$136.3
$16.48
$16.89
$66.66
$32.67
$32.79
$135.44
CASE 1: RATIO 0.9
Higher powered contract: w = 0.66; SFref = 0.7 SFroc = -0.01; Alref = 1.0
TOTEX baseline
Sharing factor
$15.85
80%
Revenue baseline
$16.24
$16.65
Additional income
$2.30
$32.60
$32.77
$32.67
Lower powered contract: w = 0.33 SFref = 0.4; SFroc = -0.005; Alref = 1.0
TOTEX baseline
Sharing factor
$15.31
45%
Revenue baseline
$15.69
$16.08
Additional income
$2.69
$32.47
$32.57
$32.51
3.5 - Calculation of Automatic Adjustment Factors to Manage Uncertainty
Managing uncertainty is a fundamental regulatory challenge, particularly for ex ante
approaches to establishing allowed revenues (see Section 1.2). While the use of an RNM
and menu of contracts produces a clear revenue determination for each utility taking into
account forecasted cost of capital, evolution of network uses, and network component
costs, the ex ante nature of this regime means there will always be uncertainty regarding
the accuracy of these forecasts. In particular, DER penetration rates are likely to be
particularly uncertain and load growth may always be higher or lower than expected.
Actual network expenditures may therefore be higher or lower than the ex ante TOTEX
baseline (Section 3.4) due to deviations from forecast, irrespective of the utility's
managerial efforts. If unmanaged, this uncertainty may either threaten the ability of the
utility to recover costs, potentially violating the firm participation constraint, or it may
leave large rents to the utility unrelated to the utility's exertion of managerial effort to
reduce costs. The longer the regulatory period, the more substantial the effects of
uncertainty can be on utility cost recovery or rent extraction (Ofgem, 2010b, 2013e).
A range of uncertainty mechanisms can be employed to manage the impacts of
uncertainty and account for divergences from forecasted evolution of network uses and
costs of inputs. For a discussion of uncertainty mechanisms available for ex ante
regulatory approaches, see Ofgem (2010a, 2013c). 9 Uncertainty mechanism options
include:
9 This section
draws heavily on those texts.
74
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
"
Indexation provisions to adjust allowed expenditures based on changes in
economy-wide price indexes (i.e., adjustment to account for economy-wide
inflation of input costs or changes in average cost of debt).
*
Full or partial pass-through of the costs of some inputs that the regulator wants
to ensure cost recovery for (i.e. smart meter roll-out costs of DG connection costs).
*
Revenue trigger provisions that specify a rule to increase or decrease revenues by
a specified amount if certain trigger events occur (i.e. achievement of certain
regulatory goals, substantial departures from forecasted network uses, etc.).
*
Re-opener thresholds specifying clear conditions under which expenditure and
revenue determinations will be revisited and revised (i.e., to account for major
new legislative requirements on utilities, changes in the tax code, substantial
departures from forecasted network uses, or other major unanticipated cost
drivers).
*
Mid-period reviews of output requirements half-way through the regulatory
period.
*
Automatic adjustment factors or forward-looking volume drivers: pre-determined
formula to adjust revenues as the volume of key cost drivers (e.g., the number of
customer connections, load growth, DER penetration) depart from ex ante
forecasts.
The sharing factor established by the menu of contracts also works to manage
uncertainty, as utilities and ratepayers share risks associated with divergences in realized
costs from ex ante forecasted costs. For example, the higher/lower the sharing factor, the
more/less financial exposure the utility faces to departures in costs from the forecast.
Appropriate uncertainty mechanisms must be carefully selected and involve a set of
important considerations (Ofgem, 2010b, 2013e). Uncertainty mechanisms can increase
the complexity of the regulatory regime, and some mechanisms (i.e., re-openers and midperiod reviews) can introduce greater regulatory uncertainty, potentially undermining
investment confidence or incentives for efficiency. Uncertainty mechanisms (particularly
pass-throughs) can also undermine incentives for the utility to manage uncertainty and
risk, reducing incentives for efficiency. The interaction of various uncertainty mechanisms
must also be carefully understood, and opportunities for utilities to game mechanisms off
one another should be minimized.
In general, utilities should remain exposed to any uncertainties for which they are well
equipped to manage. Use of uncertainty mechanisms should be limited and focused on
75
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
instances where they deliver clear value for money. Potential rationales for introduction
of uncertainty mechanisms include cases where:
*
Uncertainty may raise concerns that the utility will not be able to finance
necessary investments. Uncertainty mechanisms here can thus help reduce the
risk that a re-opener of the ex ante expenditure and revenue determinations will
be necessary to avoid violating the firm participation constraint.
*
Uncertainty mechanisms can reduce the cost of capital faced by the utility, thus
reducing prices faced by network user.
*
Key cost drivers are fully outside the utility's control (i.e., inflation indexes).
*
Uncertainty mechanisms are necessary to ensure utilities do not face disincentives
to serve network customers (i.e. automatic adjustment factors to account for
costs of DER interconnection).
This section focuses on the ex ante calculation of automatic adjustment factors, or "delta
factors," simple formulas which will be applied expost to correct the estimate of efficient
network expenditures (the TOTEX baseline) to account for any deviations from the
forecast for both load growth and DG penetration (Step 9). These delta factors are
intended to reduce incentives for the utility to slow interconnection of DERs and to
account for variations in the evolution of network uses from loads, thus reducing the risk
that the revenue determination will need to be re-opened and improving allocative
efficiency. Similar mechanisms have been employed by the Comisidn Nacional de Energia
(CNE) in Spain to account for deviations in load growth (known as the "Y factor," which is
calculated using an RNM) (Cossent & G6mez, 2013) and by Ofgem in the UK to account
for DG penetration during DPCR4 and DPCR5 (known as the "DG incentive;" the method
used to calculate of this adjustment is not transparent) (Ofgem, 2010a). This thesis builds
on these methods and provides a transparent methodology for calculation of delta factors
for both load and DG penetration.
First, the RNM is employed to estimate network investment and maintenance costs under
the full range of possible load growth and DG penetration specified in Table 3-1. Nine
uncertainty scenarios can be constructing corresponding to all possible permutations
combining the three forecasts for load growth and three forecasts for DG penetration
(the low, central, and high forecasts for each). The RNM is then run in brownfield mode
to calculate the efficient network costs under each of these uncertainty scenarios. Table
3-12 illustrates the difference in investment and maintenance costs for the simulated
Denver network, as estimated by the RNM, under each of the uncertainty scenarios as
compared to the central forecast scenario, as well as the difference in load and PV
penetration.
76
Jesse D. Jenkins (2014)
Chapter 3: Demonstrating the Proposed Regulatory Process
Table 3-12: Difference in estimated efficient network costs, load, and PV penetration across uncertainty scenarios
Load
Total New Network
Pv
Investment
penetration
Total Network
Maintenance Costs
NPV
Difference from Central Case
Scenario
(kWh)
(kW)
(US$ Overnight Cost)
Efficient
TOTEX
(US$ Annual Cost)
(M$)
-19,821,979
-10,241
-$2,184,067
-$32,994
$69.47
Low Load, Central PV
-19,821,979
0
-$1,708,074
-$27,222
$69.90
Low Load, High PV
-19,821,979
10,963
-$1,339,456
-$21,491
$70.23
Central Load, Low PV
0
-10,241
-$520,576
-$6,670
$70.97
Central Load, Central PV
0
0
$0
$0
$71.44
Central Load, High PV
0
10,963
$327,716
$5,026
$71.74
High Load, Low PV
25,481,215
-10,241
$800,338
$13,583
$72.99
High Load, Central PV
25,481,215
0
$1,277,927
$19,926
$73.43
High Load, High PV
25,481,215
10,963
$1,661,622
$24,213
$73.78
Low Load, Low PV
Next, an efficient level of TOTEX for each of this scenarios is calculated, using the same
method employed to develop the regulator's central TOTEX estimate in Section 3-2.
Finally, a two-factor linear regression is performed on the differences in estimated TOTEX,
using the difference in load and PV penetration as the explanatory variables. Regression
coefficients are obtained describing the change in TOTEX as a function of the divergence
in load (in kWh) and PV (in kW) from the central forecast. These coefficients can be
considered the automatic adjustment factors, or "delta factors," and prescribe a simple
formula for correcting the TOTEX baseline ex post in light of the actual evolution of
network uses over the regulatory period. Table 3-13 shows delta factors for the simulated
Denver network, along with the R-square values for the regression. As the table
illustrates, this regression is quite robust, with an R-square value of 0.998.
Table 3-13: Regression coefficients and delta factors for load growth and PV penetration
Regression Coefficients / Delta Factors
(NPV Efficient TOTEX)
Load growth
$0.078/kWh
PV penetration
$36.20/kw
R-square value
0.998
77
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
3.6 - The Ex Ante Regulatory Process: Applying Annual Corrections
At the conclusion of each year during the regulatory period, an ex ante regulatory
commences to adjust the utility's allowed revenues in light of the realized evolution of
system uses and utility expenditures.
First, the utility submits a detailed report on actual investment and operational
expenditures (the utility's realized TOTEX) as well as details on the evolution of system
uses (i.e., load growth and penetration of DER) (Step 1).
Next, the regulator will audit these reports to ensure their accuracy, and then compute
the automatic adjustments to the ex ante TOTEX baseline to account for any differences
in actual network use as compared to the ex ante forecast (Step 2). For each year, the
regulator calculates the annual adjustment to the TOTEX baseline in total NPV terms as
per Equation 9. This value, AdjustmentNPV, corresponds to the expected total
increase/decrease in efficient network expenditures necessary to accommodate the
increase/decrease in network use as compared to the TOTEX baseline and is calculated
for each of the key network uses for which delta factors have been computed (i.e., in this
demonstration, for both load growth and PV penetration). Since the utility would not be
expected to make all of the expenditures to accommodate this deviation from forecast in
the immediate year, this total NPV adjustment is then converted into a stream of annual
expenditures spread across R years, where R is the number of years remaining in the
regulatory period. Each annual value is inflated by the discount rate, as in Equation 6, to
arrive at a stream of annual values equal in NPV terms to the total adjustment calculated
in Equation 5.
(9) AdjustmentNPV = Delta Factor x (Deviationy - Deviationy_)
(10) Adjustmenti = (AdjustmentNPV x
*
(1 + Discount Rate)
Where:
AdjustmentNPV: total adjustment to TOTEX baseline (in NPV$)
Delta factor delta factor for network use (in NPV$/kWh for load, NPV$/kW for PV)
Deviationy: difference between realized and forecasted network use in year y
(in kWh for load and kW for PV)
Deviationy_1 : cumulative deviation from forecast through year y-1
y current year in the regulatory period
Adjustmenti: annual adjustment to allowed revenues in year iwhere 1= y.y+(R-1)
R: number of years remaining in the regulatory period
DiscountRate: regulatory discount rate
Table 3-14 provides an example of the annual adjustments to the TOTEX baseline for a
deviation in load growth from the forecast, as applied over each year of the five year
78
Jesse D. Jenkins (2014)
Chapter 3: Demonstrating the Proposed Regulatory Process
regulatory period. The same calculations would be employed for any deviations in the
penetration of PV or other DERs as per the delta factors defined in the ex ante regulatory
contract (see Section 3.5). The sum of all such annual adjustments will then be added to
each year of the ex ante TOTEX baseline to arrive at the adjusted ex post TOTEX baseline.
Table 3-14: Example of annual ex post adjustments to TOTEX baseline due to realized load growth
Delta factor for load is $0.078/kWh; regulated discount rate is 6.5%
1
2
3
4
5
Forecasted
2,186.5
2,212.3
2,238.3
2,264.8
2,291.5
Realized
2,182.7
2,204.6
2,226.7
2,249.1
2,271.6
Deviation
-3.80
-7.67
-11.64
-15.69
-19.82
-$0.32
-$0.35
-$0.38
-$0.41
-$0.07
-$0.07
-$0.08
-$0.08
-$0.09
-$0.09
-$0.10
-$0.10
-$0.12
-$0.13
-$0.14
-$0.20
-$0.22
Year
NETWORK USE: LOAD (M kWh)
ADJUSTMENT TO TOTEX BASELINE (M NPV $)
-$0.30
AdjustmentNPV
ADJUSTMENT TO TOTEX BASELINE (annual streams, in M $)
-$0.06
Year 1
Year 2
Year 3
Year 4
-$0.44
Year 5
Cumulative Adjustment
-$0.06
-$0.15
-$0.29
-$0.51
-$0.98
After calculating the adjusted TOTEX baseline, the regulator then compares the utility's
realized TOTEX over the last year with the adjusted TOTEX baseline, and the efficiency
incentive is calculated (Step 3) as per Equation 11. This is the portion of the over/underspend shared by the utility's shareholders. The ex post allowed TOTEX is thus the utility's
realized TOTEX less this efficiency incentive, as in Equation 12.
(11) Efficiency Incentive = Sharing Factor x (TOTEXbaseline - TOTEXrealized)
(12) TOTEXauowed= TOTEXrealized - Efficieny Incentive
Where:
Efficiency Incentive: portion of over/under-spend shared by utility shareholders
SharingFactor the sharing factor specified in the ex ante regulatory contract
TOTEXbaseline: adjusted TOTEX baseline
TOTEXrealized: total network expenditures realized by the utility
This efficiency incentive is the share of any cost savings or cost overruns to which the
utility should be exposed, as specified by the sharing factor in the ex ante regulatory
contract. If costs are reduced below the adjusted TOTEX baseline, the efficiency incentive
79
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
will be a negative value, indicating that the utility should be allowed to recover more than
their realized costs, thus sharing some of the cost savings with the utility's shareholders.
If realized costs are higher than the adjusted TOTEX baseline, than the utility has
overspent and the efficiency inventive will be a positive value. In this case, the utility's
allowed cost recovery should be less than the realized costs, ensuring the utility's
shareholders pay for a portion of the cost overruns.
After computing the allowed ex post TOTEX, the regulator can calculate the ex post
revenue allowance associated with this allowed TOTEX. These calculations employ the
same method discussed in Section 3.4 (Equations 2-8). Note that regardless of actual
capital expenditures, the portion of allowed TOTEX capitalized into the RAV is determined
by the slow money share set ex ante, maintaining balanced incentives for cost-saving
efficiency efforts across both CAPEX and OPEX (as discussed in Section 3.2).
Since revenues have already been collected over the course of the recently concluded
year, the regulator must adjust the utility's revenue allowance in future years to "true up"
the collected revenues and the ex post revenue allowance computed above (Step 4). The
deficit or surplus in collected revenues is calculated as the difference between allowed
cost recovery and collected revenues. Next, an annual stream of adjustments to true up
future allowed revenues is calculated as per Equation 13, which ensures that the NPV of
adjustments to future revenues corrects for the surplus or deficit in collected revenues
over the recently concluded year. This true up is applied as a stream of annual
adjustments, rather than a single lump sum correction, so as to smooth the impact on
rates and avoid discontinuous rate increase/decreases. The corrected revenue allowance
is thus the allowed revenues less the cumulative true up adjustments.
(13) True UPK = Allowed Revenues-Collected Revenues x
(1+
Discount Rate)K-y
Where:
True UpK: annual adjustment to allowed revenues in year Kwhere K= y+1:y+N
Allowed Revenues: expostrevenue allowance for y
Collected Revenues: revenues collected in yeary
y current year in the regulatory period
N: the number of years in the regulatory period
As allowed revenues are adjusted for the next N years, where N is the length of the
regulatory period, a portion of the true up corrections will be applied during the next
regulatory period. The regulator must therefore track these adjustments and add them
to the revenue baseline calculated in the next regulatory period. This N year rolling
window of true up corrections also ensures that the utility's incentives for cost savings
are equalized across each year in the regulatory period, as no matter what year these
80
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
savings are achieved, the utility will be entitled to collect the agreed share of those savings
over the next N years.
Table 3-17 (next page) demonstrates the full application of the expost annual adjustment
process and formulas described above. In this hypothetical example, the utility initially
estimates a higher efficient cost than the regulator (ratio 1.1), but ultimately achieves a
reduction in costs bringing TOTEX below the adjusted TOTEX baseline (ratio 0.9). Load
ends up growing slower than forecasted, while PV penetration grows more rapidly. This
case therefore demonstrates the computation of adjustments to the TOTEX baseline due
to realized network use as well as the adjustments to the revenue allowance to account
for efficiency incentives and the additional income allowance specified by the ex ante
regulatory contract.
Table 3-16 shows the financial position of the utility under this example. As illustrated,
because the utility was able to achieve significant cost savings, the utility's shareholders
earn a final after tax return on equity of 7.5% for the regulatory period, above the target
return on equity of 6.5%.
Table 3-16: Final financial position of the utility
Cost of debt = 5.5%; gearing ratio = 35% equity / 65% debt; tax rate
Allowed revenues
$132.8
Allowed costs
$103.0
Fast money allowance
$28.1
Depreciation allowance
$43.3
Cost of debt
$31.6
Efficiency incentive income
$5.7
Earnings before interest & taxes
$35.6
Taxes
$12.4
Net profit
$23.1
After-tax return on equity
7.5%
81
=
35%
pter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
e 3-17: Example of ex post annual corrections to allowed revenues
nte ratio = 1.1; Sharing factor = 60%; Additional income = -1.54%; Slow money share = 59%; WACC = 7.08;
ar
NPV
1
2
3
4
5
-3.80
-7.67
-11.64
-15.69
-19.82
+2,193
+4,385
+6,578
+8,770
+10,963
6
7
8
9
VIATION IN NETWORK USE FROM FORECAST
ad deviation (M kWh)
deviation (kW)
TEX (M $)
gulator's estimate
$71.4
$16.4
$16.8
$17.2
$17.7
$18.1
ility's estimate
$78.6
$18.0
$18.5
$19.0
$19.4
$19.9
alized cost
$63.2
$14.51
$14.88
$15.25
$15.63
$16.02
ANTE TOTEX AND REVENUE BASELINES (M $)
TEX baseline
venue baseline
$73.9
$17.0
$17.4
$17.8
$18.3
$18.7
$135.3
$32.3
$32.4
$32.6
$32.7
$33.0
MULATIVE ADJUSTMENT TO TOTEX BASELINE DUE TO DEVIATIONS FROM FORECASTED NETWORK USE (M $)
justment: load
-$1.54
-$0.06
-$0.15
-$0.29
-$0.51
-$0.98
justment: PV
+$0.40
$0.02
$0.04
$0.08
$0.13
$0.25
justed TOTEX baseline
$72.7
$16.9
$17.3
$17.6
$17.9
$18.0
LCULATION OF EFFICIENCY INCENTIVE AND EX POST ALLOWED TOTEX
al over/under-spend
-$9.52
-$2.40
-$2.40
-$2.36
-$2.26
-$1.97
iciency incentive
-$5.71
-$1.44
-$1.44
-$1.42
-$1.36
-$1.18
post allowed TOTEX
$68.9
$16.0
$16.3
$16.7
$17.0
$17.2
Adjustments applied in next regulatory period
RRECTION OF REVENUE ALLOWANCE (M $)
post allowed revenues
$132.8
$31.9
$31.9
$32.0
$32.0
$32.1
venue correction
-$1.57
$0.00
-$0.09
-$0.18
-$0.28
-$0.39
-$0.51
-$0.43
-$0.34
-$0.24
-$0
al revenue allowance
$132.8
$32.27
$32.22
$32.20
$32.19
$32.18
-$0.51
-$0.43
-$0.34
-$0.24
-$
82
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
3.7 - Evaluating the Performance of the Regulatory Process
This section evaluates the performance of the regulatory process proposed herein,
demonstrating the incentive compatibility of the overall process, the ability of the process
to accommodate uncertainty in the evolution of network uses, and the performance of
the framework if the regulator errs in establishing their ex ante TOTEX baseline.
Tables 3-18 and 3-19 demonstrate the incentive-compatibility of this regulatory method
across a range of scenarios for the simulated Denver system. For any ex post realized
TOTEX, the utility earns the greatest net profit, expressed as earnings before interest and
taxes (EBIT) and after tax return on equity when realized expenditures align with the ex
ante estimate submitted by the firm at the beginning of the regulatory period (see the
highlighted cells in Tables 3-18 and 3-19). As such, this approach removes any incentive
for the firm to engage in strategic behavior by inflating their ex ante estimate of network
expenditures in an effort to increase their profits; such an effort would, in fact, reduce
the firm's net profit and return on equity under this regulatory approach. This method
thus encourages the utility to submit the highest-quality, most accurate business plan
they can develop. Along with the use of an RNM to develop the regulator's estimate, the
incentive-compatibility of this regulatory approach substantially reduces the information
asymmetry between regulator and firm.
Table 3-18: Incentive compatibility with higher-powered menu of contracts
EX ANTE TOTEX ESTIMATES
Regulator Estimate
NPV M$
$71.4
$71.4
$71.4
$71.4
Firm's Estimate
NPV M$
$64.3
$71.4
$78.6
$85.7
%
90
100
110
120
Ratio (Firm/Regulator)
EX ANTE REGULATORY CONTRACT (w = 0.66;
SFref = 0.7 SFroc=
-0.01;
Alref = 1.0)
TOTEX baseline
NPV M$
$69.0
$71.4
$73.9
$76.3
Revenue baseline
NPV M$
$136.3
$135.9
$135.3
$134.5
%
80
70
60
50
NPV M$
$2.3
$0.7
-$1.1
-$3.1
Sharing Factor
Additional Income
EX POST RESULTS
Realized TOTEX
(NPV M$)
TOTEX Ratio
(Realized/
Regulator)
$64.3
90
$24.04 / 7.78
$23.83/ 7.70
$23.16 / 7.47
$22.00 / 7.10
$71.4
100
$20.36 / 6.58
$20.63 / 6.65
$20.44 / 6.57
$19.76 / 6.35
$78.6
110
$16.67 / 5.38
$17.44 / 5.60
$17.72 / 5.68
$17.53 / 5.61
$85.7
120
$12.99 / 4.18
$14.24 / 4.56
$15.00 / 4.79
$15.29 / 4.87
Earnings Before interest and Taxes (NPV M$) /
Return on Equity (%)
83
Jesse D. Jenkins (2014)
Chapter 3: Demonstrating the Proposed Regulatory Process
Table 3-19: Incentive compatibility with lower-powered menu of contracts
EX ANTE TOTEX ESTIMATES
Regulator Estimate
NPV M$
$71.4
$71.4
$71.4
$71.4
Firm's Estimate
NPV M$
$64.3
$71.4
$78.6
$85.7
%
90
100
110
120
Ratio (Firm/Regulator)
EX ANTE REGULATORY CONTRACT (w = 0.66; SFref = 0.7 SFroc = -0.01; Alref
= 1.0)
TOTEX baseline
NPV M$
$66.7
$71.4
$76.2
$81.0
Revenue baseline
NPV M$
$135.4
$135.9
$136.5
$137.2
%
45
40
35
30
NPV M$
$2.7
$0.7
-$1.1
-$2.9
Sharing Factor
Additional Income
EX POST RESULTS
Realized TOTEX
(NPV M$)
TOTEX Ratio
(Realized/
Regulator)
$64.3
90
$22.46 / 7.29
$22.39 / 7.25
$22.07 / 7.14
$21.51 / 6.95
$71.4
100
$20.47/ 6.61
$20.63 / 6.65
$20.56 / 6.61
$20.24 / 6.50
$78.6
110
$18.47 / 5.94
$18.88 / 6.05
$19.05 / 6.09
$18.97 / 6.06
$85.7
120
$16.48 / 5.27
$17.13 / 5.46
$17.58 / 5.58
$17.69 / 5.62
Earnings Before Interest and Taxes (NPV M$) /
Return on Equity (%)
In addition, this regulatory method provides the firm with clear incentives to reduce costs
to the most efficient level possible (subject to quality of service requirements or
incentives): in either table, moving upwards in any column always increases the firm's
profits and return on equity. As can be seen by comparing Tables 3-18 and 3-19, the
strength of the efficiency incentives are determined by the construction of the menu of
contracts, and the regulator thus has substantial flexibility in determining the appropriate
level of incentives (see Section 3.3).
Because this method adopts the TOTEX-based approach to capitalizing expenditures
pioneered by Ofgem (i.e., "fast money" and "slow money" shares are fixed ex ante, see
Section 3.2 and Ofgem, 2009, pages 117-120, and Ofgem, 2013a, pages 30-32), incentives
for cost-savings across CAPEX and OPEX are equalized. Table 3-20 demonstrates the net
profit (earnings before interest and taxes) of the utility under two cases, both of which
reduce total net present value of network expenditures by 5 percent: a case where
savings are achieved by reducing CAPEX and a case where equal savings are achieved by
reducing OPEX. As the table demonstrates, if the utility's assets are capitalized based on
weaker
the realized CAPEX (the "CAPEX method"), incentives to reduce CAPEX costs are
than for OPEX. In this case, the firm would earn $150,000 more over the regulatory period
by reducing OPEX instead of CAPEX, distorting tradeoffs between these two expenditure
84
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
categories. If instead, the TOTEX-based method is applied, in which the share of realized
expenditures capitalized into the utility's regulated asset value is fixed ex ante by the
regulator (the "slow money share"), cost-saving incentives are balanced and the utility's
profits are equalized under both savings cases.
Table 3-20: Example of balanced and distorted incentives for CAPEX and OPEX cost-savings
Sharing factor: 70%; Additional income: 1%; Gearing ratio: 35% equity / 65% debt; WACC: 7.08%
Value
Unit
No savings
case
CAPEX
savings case
OPEX
savings case
CAPEX
NPV M$
$42.32
$38.75
$42.32
OPEX
NPV M$
$29.13
$29.13
$25.55
TOTEX
NPV M$
$71.44
$67.87
$67.87
Ex ante slow money share
%
59%
59%
59%
Ex post CAPEX share
%
59%
57%
62%
Distortion
EARNINGS BEFORE INTEREST AND TAXES
TOTEX method
NPV M$
$20.63
$22.23
$22.23
None
CAPEX method
NPV M$
$20.63
$22.17
$22.32
$0.15
In addition to providing clear incentives for the firm to provide an accurate ex ante
business plan and to achieve cost-saving efficiencies throughout the regulatory period,
the use of the delta factors described in Section 3.6 also effectively manages uncertainty
in the evolution of network use. Table 3-21 illustrates the performance of these delta
factors for the simulated Denver system. Across a set of uncertainty scenarios
encompassing a deviation of plus or minus 1 percent in realized load and 11 percent in PV
penetration, the delta factors correct the TOTEX baseline to within 1.2 percent of the
realized network expenditures (as estimated by the RNM). In most cases, the deviation
between realized costs and the adjusted TOTEX baseline less than 0.1 percent. As
Table 3-20 illustrates, the error appears to increase non-linearly. This may indicate that a
non-linear functional form for the delta factors may improve performance. Further
research should explore the performance of these delta factors across a wider range of
variation in network use and explore alternative functional forms. However, even the
simple linear delta factors employed herein substantially reduce the impacts of
uncertainty in network use, maintaining efficiency incentives and ensuring the firm can
recover reasonably incurred expenditures throughout the regulatory period. This is a
considerable advantage for an ex ante regulator approach.
85
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
Table 3-21: Performance of delta factors (automatic adjustment factors)
NETWORK USE SCENARIOS
Load
Low
Low
Low
Central
Central
Central
High
High
High
PV
Low
Central
High
Low
Central
High
Low
Central
High
NETWORK USE REALIZED VALUES (Load: million kWh; PV: kW-peak)
Load
2,272
2,272
2,272
2,292
2,292
2,292
2,317
2,317
2,317
PV
85,100
95,341
106,304
85,100
95,341
106,304
85,100
95,341
106,304
PERCENT DEVIATION IN REALIZED NETWORK USE VS CENTRAL FORECAST
Load
-1
-1
-1
0
0
0
+1
+1
+1
PV
-11
0
+11
-11
0
+11
-11
0
+11
TOTEX (NPV million $)
Realized
$69.47
$69.90
$70.23
$70.97
$71.44
$71.74
$72.17
$72.60
$72.95
Adjusted
$69.53
$69.90
$70.30
$71.07
$71.44
$71.84
$73.06
$73.43
$73.82
+0.1
+0.0
+0.1
+0.1
0.0
+0.1
+1.2
+1.1
+1.2
Baseline
Error (%)
While the delta factors successfully address forecast error, this method is still susceptible
to benchmark error: that is, if the regulator errs in establishing their ex ante estimate of
the efficient frontier for network costs due to a defect in the RNM or another error on the
regulator's part, the TOTEX baseline may be set too high or too low, distorting efficiency
incentives.
Ideally, the combination of the regulator's estimate and the firm's estimate of efficient
TOTEX will blunt the impacts of benchmark error. The incentive compatibility of the menu
of contracts will encourage the utility to submit their most accurate TOTEX estimate. As
this estimate is averaged with the regulator's estimate as per the weighting factor, w, the
firm's estimate can counteract a portion of any benchmarking error on the regulator's
part when setting the ex ante TOTEX baseline. However, the additional income computed
to ensure the incentive compatibility of the menu of contracts also depends on the
weighting factor placed on the regulator's estimate as per the equations in Table 3-5. This
counteracts the impact of incorporating the firm's estimate into the TOTEX baseline on
the error in final allowed profits and return on equity, as illustrated in Table 3-22. Two
scenarios are depicted in this table: one in which the regulator under-estimates efficient
costs by 20 percentage points and the other where the regulator over-estimates efficient
costs by an equivalent percentage. In both cases, the utility's ex ante forecast is treated
as accurate. Three different weighting factors are also shown, with the error in profit and
after tax return on equity computed. As Table 3-22 demonstrates, the error is barely
impacted by the choice of weight placed on the regulator's TOTEX estimate. Counterintuitively then, the weighting factor cannot help mitigate the impacts of benchmark error
86
Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
using this method. Future research may wish to identify a method for constructing an
incentive compatible menu of contracts which does not have this feature.
Table 3-22: Performance of regulatory method under benchmark error with different weighting factors
SFref = 0.7 SFroc = -0.01; Alref = 1.0
+20 percent
-20 percent
Regulator Error
1.0
0.66
0.33
1.0
0.66
0.33
Allowed Profit /
Return on equity
$15.23 /
4.9
$15.29 /
4.9
$15.35 /
4.9
$28.03 /
9.0
$27.93 /
9.1
$27.82 /
9.1
Accurate Profit /
Return on equity
$20.98 /
6.6
$20.98 /
6.6
$20.98 /
6.6
$20.29 /
6.7
$20.29 /
6.7
$20.29 /
6.7
Error in profit /
Return on equity
-$5.74 /
-1.8
-$5.69 /
-1.8
-$5.63 /
-1.8
+7.74 /
+2.3
+$7.64 /
+2.4
+7.53 /
+2.4
Weighting factor (o)
While the weighting factor cannot help mitigate the impacts of benchmark error, the
regulator can select the reference sharing factor parameter (SFref) to reduce the impacts
of any errors in the regulator's forecast. As Table 3-23 demonstrates, the error in allowed
profits and return on equity declines as the sharing factor declines (as anticipated by
Schmalensee (1989)). This result is expected, as the lower the sharing factor, the closer
the regulatory contract becomes to a cost-of-service contract, and thus the less sensitive
the firm's profits are to realized costs.
Table 3-23: Performance of regulatory method under benchmark error with different sharing factors
w = 0.66; SFroc = -0.01; Alref = 1.0
+20 percent
-20 percent
Regulator Error
1.0
0.7
0.4
1.0
0.7
0.4
Allowed Profit /
Return on equity
$12.44 /
4.0
$15.29 /
4.9
$18.15 /
5.8
$30.78 /
10.0
$27.93 /
9.1
$25.07 /
8.2
Accurate Profit /
Return on equity
$20.98 /
6.6
$20.98 /
6.6
$20.98 /
6.6
$20.29 /
6.7
$20.29 /
6.7
$20.29 /
6.7
Error in profit /
Return on equity
-$8.54 /
-2.7
-$-5.69 /
-1.8
-$2.83 /
-0.9
+10.49/
+3.3
+$7.64 /
+2.4
+4.78 /
+1.5
Sharing factor (SFref)
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Jesse D. Jenkins (2014)
3.8 - Summary of Advantages of the Proposed Regulatory Process
This chapter demonstrates, step-by-step, the practical implementation of a novel
regulatory method combining several state-of-the-art regulatory methods, including: use
of an engineering-based reference network model (RNM) for forward-looking
benchmarking of efficient network expenditures; an incentive compatible menu of
contracts to elicit accurate forecasts from the utility and create incentives for cost saving
efficiency efforts; and ex post automatic adjustment mechanisms, or "delta factors," to
accommodate uncertainty in the evolution of network use and minimize forecast error.
This approach yields several important advantages for the economic regulation of electric
distribution utilities under increasing penetrations of distributed energy resources, which
are summarized below.
Reduces information asymmetry and minimizes opportunities strategic behavior
" The regulatory regime proposed herein helps overcome information asymmetry
by equipping the regulator with a reference network model with which to develop
their estimate of efficient network expenditures. The RNM emulates the network
planning practices of an efficient utility and can help the regulator develop more
accurate estimates of future network costs given the expected evolution of
network uses.
*
Combining the use of an RNM with an incentive compatible menu of contracts
further reduces information asymmetry by incentivizing the utility to submit their
most accurate estimate of future network expenditures. The incentive compatible
property of the menu of contracts thus eliminates incentives for the utility to
engage in strategic behavior by inflating their estimate of necessary TOTEX, a
significant advantage over other ex ante regulatory approaches that do not
employ a menu of contracts.
Helps manage systemic uncertainty
*
Because the RNM creates a forward-looking benchmark for efficient total network
expenditures (TOTEX), the model can be designed to accommodate expected
evolutions in network use, technology costs, and network management practices,
reducing the uncertainty facing the regulator. In effect, the RNM gives the
regulator a tool with which to "peer into the future," a crucial ability in ex ante
regulatory approaches. This forward-looking capability stands in contrast to
statistical benchmarking techniques, which rely on backward-looking analysis of
realized expenditures during prior regulatory periods and thus cannot capture the
dynamic changes now unfolding in the electricity distribution sector.
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Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
"
The RNM can also be used to explore a range of possible scenarios for the
evolution of network uses (i.e., load growth and DER penetration). The model
results can then be used to compute delta factors, simple formulas to
automatically adjust the efficient TOTEX baseline in light of the realized evolution
of network use. These delta factors effectively minimize the impacts of forecast
errors, a significant advantage given increased uncertainty about the likely
evolution of network use over the coming years.
"
By selecting the strength of the profit sharing factor, the regulator can also help
mitigate the impacts of benchmark error - i.e., an error in the regulator's estimate
of efficient TOTEX (irrespective of the evolution of network use). The lower the
sharing factor, the closer the regulatory contract becomes to a cost-of-service
contract, and thus the less sensitive the firm's profits are to differences in
forecasted and realized costs, and vice versa. The regulator can thus select an
appropriate sharing factor based on their confidence in the accuracy of their
forecasts of efficient network expenditures.
Incentives cost savings and overcomes moral hazard
*
The profit sharing parameter established by the menu of contracts creates clear
incentives for the utility to seek cost-saving efficiency measures throughout the
regulatory period. This profit sharing incentive gives the utility's management and
shareholders a direct stake in cost-saving measures and thus overcomes the moral
hazard problem that plagues cost-of-service regulation. In addition, these cost
savings can help offset the investments that are likely necessary to accommodate
changes in network use, improve network resilience, and implement smart grid
and active system management techniques, all of which are growing priorities for
various regulatory jurisdictions. Furthermore, the regulator can establish the
strength of the efficiency incentives as desired through the design of the menu of
contracts (i.e., setting the SFref and SFroc discretionary parameters).
*
The "slow money/fast money" approach to capitalization of allowed ex post
network expenditures also equalizes incentives for the firm to optimize costsaving tradeoffs between network investments (CAPEX) and operational
expenditures (OPEX). Without this approach, the utility may face distorted
incentives that encourage over-spending on network assets in lieu of cost saving
operational expenditures, including innovative contractual arrangements with the
owners of distributed energy resources. Removing this distortion and equalizing
cost-saving incentives across both categories of network expenditures is thus an
important step to encouraging cost-saving active system management
approaches and encouraging an evolution in the distribution utility business
model.
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Chapter 3: Demonstrating the Proposed Regulatory Process
Jesse D. Jenkins (2014)
Balances fundamental regulatory tradeoffs
*
The regulator has significant flexibility and discretion to set the strength of the
sharing factor parameters used to create the menu of contracts in order to
balance the fundamental regulatory tradeoffs between allocative efficiency
(extracting rents from the utility) and X-efficiency (providing incentives for cost
savings).
"
Furthermore, the incentive
" compatible nature of the menu of contracts will encourage firms with significant
cost-saving opportunities to select a higher-powered incentive (thus improving Xefficiency) while firms closer to the efficient frontier will select a lower-powered
incentive (improving allocative efficiency). Firms thus reveal their cost type, and
the resulting regulatory contract appropriately balances the moral hazard and
adverse selection challenges.
Finally, it is important to note that this regulatory approach only considers the
establishment of allowed TOTEX and the primary allowed revenues. These methods must
be accompanied by appropriate incentives for the utility to maintain and improve quality
of service, meet other performance expectations (including customer service quality and
environmental performance) and engage in long-term innovation efforts (which are not
adequately incentivized by the relatively short-term cost-savings incentives created
herein. (For more on these important considerations, see: Bauknecht, 2011; Cossent,
2013; Lo Schiavo, et al., 2013; Ofgem, 2010c, 2013c, 2013d; Perez-Arriaga et al., 2013).
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