In a Time of Transition, Regulators Can Drive Cutting-Edge Cost Allocation Reform

The need for change in how we measure the cost of providing electric service by customer class is obvious to any attentive observer of the dramatic changes now underway in the electric utility industry. Electric Cost Allocation for a New Era, the manual RAP published at the beginning of 2020, provides detailed guidance on the preparation of embedded and marginal cost studies. It also gives guidance on how to use today’s dramatically better data to ensure that these studies reflect changing technologies, costs and usage patterns.

This task falls mostly to utility technical analysts who prepare these studies, and to parties in rate cases who analyze them. But a significant duty falls on utility regulators to set policy and insist on visionary and rigorous analysis of new issues.

Today we have resources such as wind, solar and storage, providing energy and capacity in different ways and with different cost and operating characteristics than fossil energy resources. We are redeploying transmission lines, originally built to connect remote coal and nuclear baseload power plants, to instead support variable renewable resources and facilitate market transactions that reduce energy costs. Where once we required remote generation and transmission, we now have smarter distribution networks and distributed energy resources that provide modern energy, capacity and grid services. All of this points to the need for reforms in cost allocation.

The modern cost allocation study must do many things that studies of past years may not have considered:

• Separate the treatment of different kinds of generating plant, such as baseload, intermediate, peaking and non-dispatchable variable renewables.
• Recognize the multiple purposes of transmission facilities: to connect baseload units, to connect remote baseload and variable generation, and to facilitate market transactions buying energy from the nearby market where it’s cheapest.
• Identify the nature of distribution system component functions, no longer simply to connect customers to a centralized grid, but also to facilitate demand response, energy efficiency, time-shaping of consumption, and integration of distributed energy resources that may serve all customers.
• Recognize a wide variety of costs that have benefits across functional areas, including demand-side resources of all kinds and advanced metering.

The modern cost study requires better data and more attention to detail on the part of the cost analyst. While federal law has included a PURPA standard for time-of-use cost allocation analysis since 2005, nearly all cost studies prepared a decade and a half later continue to ignore this. Fortunately, modern smart meters and the associated data collection systems now in place for more than half of U.S. electricity consumers can quite easily provide the needed granular data to support time-of-use analysis.

This allows utilities to measure individual and class loads on an hourly basis and unlocks a wide array of potential analytical improvements. For example, baseload resource costs can be properly assigned to all hours, and other generation costs on a time-varying basis — to the appropriate hours when they are used to provide service.

The modern utility regulator is also faced with a broader challenge of an energy system transition including the gradual introduction of improved customer class definitions and improvements to rate design. Cost allocation studies, and particularly the underlying analyses in those studies, can be a core part of these important reforms. The result should be a set of customer classes, cost allocations, and ultimately rates that properly reward the types of cost-minimizing behavior that is now available. Customers able to flex their loads to fit the least costly time periods should be able to do so, and when they do, should reduce the costs allocated to their class and to their own consumption. This will include:

• Customers shifting loads, such as space heating and cooling, water heating, electric vehicle charging, industrial process needs, and ultimately, smart appliances down to and including laundry, lighting and data processing.
• Customers installing on-site energy storage, which may be controllable by the utility to provide grid services in addition to customer services.
• Behavioral changes in consumption due to time-varying cost periods flowing into time-varying pricing.
• Cost savings in generation, transmission and distribution cost from a network that takes advantage of both central and distributed resources, increasingly functioning as a well-choreographed ballet involving efficient markets, efficient data transfer and efficient consumer actions.

Achieving these goals will not be easy. It will require smart systems, smart system operators and smart regulators. The technology to help achieve all of this is available. The skills to use modern data, design modern studies and implement smart changes may be elusive without significant guidance from regulators. Regulators will also need to maintain oversight to ensure that data collection, data analysis, costing model design, presentation of study results, and proposals for changes in class definition and cost allocation are done in an open, collaborative and constructive fashion. This can maximize both the net benefits to be achieved and the equitable sharing of those benefits.

Modern Marginal Cost of Service Studies

The previous blog post in this series examined embedded cost of service studies — but some states choose to look ahead, considering marginal costs. This approach stems from the economic theory that today’s consumption drives tomorrow’s costs and customer classes should be responsible for the future impact of their usage. These states, notably California, Oregon and Nevada, require utilities to base cost allocation on marginal cost of service studies.

A marginal cost of service study typically measures the cost of expanding system capability to meet additional requirements for capacity at peak periods, additional transmission and distribution capacity, and additional energy usage, with at least energy-related costs differentiated by time period. Just as it does for embedded cost studies, our manual, Electric Cost Allocation for a New Era, features an extensive discussion of best practices in completing marginal cost studies.

Key principles for the cost analyst to keep in mind for modern marginal cost studies include:

• The time horizon examined should be consistent across all of the cost elements.
• Capital costs often substitute for short-run variable costs, for example in the choice of wind or solar generation as an alternative to burning fuel in conventional power plants.
• If the capital cost of new distribution service extensions to serve new customers is included, then the capital cost of new generation and transmission facilities must also be included.
• Demand response, not new generating capacity or storage capacity, may be the lowest-cost capacity resource to meet short-duration peak loads.
• Smart grid technologies, including smart meters and data managements systems, are used to save energy, to reduce capacity requirements, to collect data for system planning and to bill customers.

The “NERA methodology,” named for the consulting firm that originally developed it in the late 1970s, has many shortcomings in today’s electric system marked by significant quantities of wind, solar, storage, and demand response providing both energy and localized capacity services. Analysts using this dated method will need to update their models to reflect current resource options.

A common error in marginal cost studies is the failure to recognize that temporary excess capacity in one or more parts of the system — generation, transmission, or distribution — may distort the results of the study. This is a particular problem in systems where renewable portfolio standards may be causing the development of new generating capacity before it is required for reliability purposes. This can result in a temporary excess of capacity that distorts cost study results.

New problems emerge in a modern grid, where some particularly flexible capacity may be needed to provide “ramping” service during hours when renewable generation drops off, rather than to meet peak-hour needs. The analyst needs to understand this to properly determine how these capacity costs should be treated in the cost study.

With a marginal cost study in hand, the analyst needs a means for reconciling its findings to the allowed revenue requirement. Two methods are typically used, one that relies on the “inverse elasticity rule,” and a second, more commonly used, that employs the “equal proportion of marginal cost” framework. A key finding of the handbook is that the better choice is to reconcile cost elements within functional categories — that is to ensure that generation, transmission, and distribution costs are adjusted to reconcile with the revenue requirement within those categories, rather than across all costs as a whole. This avoids inequitable shifting of cost responsibility between customer classes, a common problem if generation or transmission investment is temporarily in surplus or shortfall.

As we’ve made clear here and in our previous posts, the transformative technological changes in the power sector — including the amount of new data now available — mean that modern cost allocation studies can, and must, take into account factors that older approaches could not and, in that old world, did not need to.

In our next, and last, post in this series, we’ll take a big-picture look at this changing landscape and recommend more cutting-edge ways in which regulators can ensure a more efficient and equitable assignment of utility costs.

Updating Embedded Cost of Service Studies for a New Era

In the first blog post in this series, we outlined how our new manual, Electric Cost Allocation for a New Era, looks comprehensively at the process of dividing utility costs among customer classes — and proposes several ways it should be updated to account for modern features of the power system. In this second installment, we’ll dive a bit deeper into the cost allocation process, examining one of the two major frameworks traditionally used in it: the embedded cost of service study, or ECOSS.

An ECOSS is a static snapshot approach, where a utility’s embedded cost revenue requirement for one year is divided among the customer classes. There are three steps in a traditional embedded cost of service study, as shown in the figure below: functionalization, classification and allocation.

Before we go into each step in more detail, we should note a caveat: The steps aren’t necessarily dependent on each other. Although they are convenient parts of organizing a cost of service study, functionalization and classification decisions are not necessarily critical to the final class cost allocations. The cost of service study can get to the same final allocation in several ways. For example, consider the reality that a portion of transmission costs may be driven by the potential cost savings from interconnecting remote generation to avoid higher fuel transportation costs. This can be reflected by:

1. Functionalizing a portion of transmission cost as generation, or
2. Classifying a portion of transmission in the same manner as the remote generation, or
3. Using a systemwide transmission allocator with some energy component.

So while it’s fair to say that this framework can be stretched, it’s still useful to weigh each step in order and reflect on where the process could benefit from considering new data, new technology and new opportunities.

In the functionalization step, the analyst divides costs into at least three functions. Generation, transmission and distribution are always separated, but good practice may dictate additional functions going forward, such as billing collection, customer service, administrative and general costs, and public policy programs. Maintaining these additional functions can allow the analyst to treat these costs more flexibly in the classification and allocation steps.

In addition, analysts should recognize that many newer technologies and programs — including demand response, energy efficiency, energy storage, distributed generation and smart grid investments — provide services that span generation, transmission and distribution.

The classification step separates each type of investment and expense, or cost, depending on the factor that prompted that cost. Most embedded cost of service studies classify costs as being either demand-related, energy-related or customer-related.

These categories have always been a simplification. Often, analysts would classify all investment and annual maintenance costs for generation as demand-related, and only short-term variable costs such as fuel as energy-related. But this should also be reevaluated in light of changes to the industry, in particular the data and analytical techniques now available. For example, wind and solar provide benefits that do not necessarily accrue at peak hours — the underlying justification of a demand-related classification. Newer methods are available to recognize the differences between baseload, intermediate, peaking, and variable renewable generation.

In the past, many analysts would commonly classify distribution investment as exclusively demand-related, or customer-related. Investments in distribution plant provide a multitude of benefits at all hours, however, and newer methods are available to assure that these costs are classified in a manner that recognizes these values.

The final step, allocation, applies an allocator to each cost category, which splits the relevant costs among the customer classes based on metrics measuring how they use the system. As may be logical, customer-related costs are traditionally divided based on metrics of customer number (often weighted by the size and complexity of customers), energy-related costs on metrics of energy usage, and demand-related costs on metrics of demand.

“Demand” metrics have been particularly varied and complex, including various measures of contribution to the annual coincident peak, the average of several high-load monthly coincident peaks, the average of all 12 monthly coincident peak contributions, some metrics of class or individual customer non-coincident peak, the average of class contribution to some number of high-load hours, and demand at all hours of the year (average demand). In addition, a “labor” allocator may be used for certain costs, such as pensions, and a revenue allocator may be used for a wide range of costs as well.

Debates have ensued for decades over elements of ECOSS studies, including:

• The functionalization of transmission investments considering the generation integration function and network integration functions;
• The classification of generation as demand-related vs. energy-related;
• The classification of distribution plant as customer-related vs. demand- or energy-related;
• The choice of demand allocators; and
• The apportionment of administrative and general costs, energy efficiency costs, and informational and educational expenses.

Today there are new challenges driven by emerging technology, including:

• Utilizing the availability of data on hourly usage by class, substation, feeder, transformer, and each consumer;
• Treatment of variable renewable energy resources, with very high capital costs and very low ongoing operating costs;
• Treatment of energy storage costs, which substitute a capital investment for a combination of capital and fuel costs for peaking resources;
• Treatment of demand response expenses, which may substitute a small payment for curtailment in place of a significant investment for supply-side capacity; and
• Recognizing the generation and transmission benefits of some distribution investments, including some abilities of smart meters, and investments supporting distributed generation, sharing excess energy among customers, and facilitating microgrids.

Electric Cost Allocation for a New Era addresses each of these issues, providing analysts with best practices in the functionalization, classification and allocation of utility costs in embedded cost of service studies. New techniques are almost certainly necessary to fairly and efficiently allocate costs to all customers, and we discuss many options. In our next blog post, we’ll move on to do the same for the other frequently used framework: the marginal cost of service study, or MCOSS.

Cost Allocation: New Approaches for a New Era

Setting electric utility rates has traditionally been a three-step process: first, determining the revenue requirement; second, determining how to equitably divide costs among classes of ratepayers; and third, designing the rates themselves.

That second step, cost allocation, is a process that has been around for decades, and many jurisdictions have created detailed precedents for how it should be carried out. But these precedents were designed for the power system of the past, and today’s rapidly changing regulatory and technological landscape undermines the foundations of these methods.

To reflect modern technologies—such as wind, solar, storage, demand response, and smart grid systems—cost allocation techniques require significant updating. That’s why we have produced a comprehensive new manual, Electric Cost Allocation for a New Era, which describes the foundations of cost allocation, explains current best practices and sketches out the direction that it should travel in the future.

Cost allocation processes rely on two primary types of analytical studies: embedded cost of service studies (ECOSS) and marginal cost of service studies (MCOSS). A typical ECOSS first divides up all the costs reflected in the revenue requirement into several different functions, including generation, transmission and distribution. Then the costs within each function are classified as energy-related, demand-related or customer-related. Finally, these costs are allocated among the different rate classes, typically including residential, small commercial, large commercial, industrial and street lighting.

The analysis for an MCOSS starts with a similar functionalization step, but it is followed by estimation of marginal unit costs for each element of the system, calculation of a marginal cost revenue requirement (MCRR) for each class as well as for the system as a whole, and then reconciliation with the annual embedded cost revenue requirement.

While the best versions of these analytical techniques were based on solid theories and good practical experience, they used simplifications that were justified by the typical features of the electric system when they were developed. Our electric system at the beginning of the 21^st century, however, has a wide array of new features that do not fit within the assumptions of these older analytical techniques. These new features include:

• Distributed energy resources that provide benefits across the generation, transmission and distribution functions;
• Investments in wind and solar that primarily provide energy benefits, rather than peak-serving benefits; and
• Advanced metering infrastructure that provides a wide array of benefits beyond customer billing, including reliability and energy benefits.

We can develop new analytical techniques that account for these new realities. As a starting point, there are two high-level principles for cost allocation that help guide the way:

1. Cost causation: Why were the costs incurred?
2. Costs following benefits: Who is better off because the cost was incurred?

In some cases, these two conceptual frameworks point to the same answer, but in other cases they don’t. When they conflict, we believe that “costs follow benefits” should usually, but not always, take priority. The new manual considers these questions for a wide range of electric utility costs. While there are multiple reasonable techniques for each issue, there are high-level best practices that apply to both embedded and marginal cost of service studies:

• Apportion all shared generation, transmission, and distribution assets and the associated operating expenses on measures of usage, both energy- and demand-based.
• Ensure broad sharing of administrative and general costs, based on usage metrics.
• Eliminate any distinction between “fixed” and “variable” costs, as capital investments (including new technology and data acquisition) are increasingly substitutes for fuel and other short-run variable operating costs.
• Treat as customer-related only those costs that actually vary with the number of customers, a technique generally known as the basic customer method.
• Where future costs are expected to vary significantly from current costs, make the cost trajectory an important consideration in the apportionment of costs.

Cost allocation may be as much art as science, since fairness and equity often lie in the eye of the beholder. In the first instance, cost allocation is a zero-sum process, where lower costs for any one group of customers lead to higher costs for another group. Thus, the best techniques used in cost allocation have been designed to fairly mediate these disputes between competing sets of interests. Applying both judgment and computation in a framework that accounts for the complexities of the modern power system will result in fairer and more equitable outcomes for all ratepayers. In addition, the cost allocation process can provide important inputs to rate design. One way it can do so is to identify seasonal and hourly cost variation by function and customer class, considering the time-varying portions of both capital investments and short-run variable costs.

This blog is the first of a series that will delve into today’s cost allocation issues in more detail, offering a path forward for regulators, utilities and stakeholders to incorporate newer, more innovative methods into the cost allocation process.