load profiling for retail choice: examining a complex and crucial component of settlement

December 2000

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Load Profiling for Retail Choice: Examining a Complex and Crucial Component of Settlement

For all its limitations, load profiling should be viewed as a good transition strategy until maturing technologies bring down the cost of advanced metering devices and small customers are ready to invest in metering hardware and software.

Jeffrey Bailey

n a newly competitive electricity market, energy suppliers (both

local distribution companies, or LDCs, and retail suppliers) face challenges on many business and information technology fronts. Companies must struggle to streamline operations, comply with new regulations, and introduce new capabilities into legacy envi-ronments, just to name a few. With much to accomplish and little time to accomplish it as Retail Choice dates come and go in deregulating regions, executives have really only one option: prioritize. First and foremost, executives are focusing

on their companies’ abilities to accurately sell or re-sell electricity—efficiently and profitably. Key to this business exchange is the settle-ment process, which lately has been placed at the top of utilities’ back office “to-do” lists nationwide. Consider the high-level implica-tions in a retail environment: busi-ness processes surrounding the physical and financial exchange of energy suddenly involve many external entities; new software is required to handle a now exceed-ingly complex, once-manual pro-cess; and regulatory rules change constantly. (And just to make things

Jeffrey Bailey

is a Senior Consultantwithin the Systems Integration practice

of American Management Systems’Utilities Consulting & Systems Group,Fairfax, Virginia. His areas of primary

focus are large-account billing systems,and load profile and settlement systems

for electric and gas utilities andunregulated energy service providers.

He has an extensive background insystems integration and custom

software development.

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The Electricity Journal

more complicated, those rules are different for everyone playing the game.) So it is clear why settlement has taken center stage. Each of its components—scheduling, load profiling, and financial settlement—is vital to the efficient, profitable, exchange of energy. But one of these, if not fully understood and not taken into consideration early, stands out as a true thorn: load pro-filing. This article examines load profiling as a lynchpin in the settle-ment process, and reveals its com-plexities to organizations making (or preparing to make) the transi-tion to Retail Choice.

I. Settlement: An Up-to-Date Overview

We know how it worked in the past: The generation, transmission, and distribution of energy were all handled by one company. It was not ill-conceived to handle settle-ment manually, and to place responsibility and understanding of this function in the hands of a very small number of people within the organization. Now things are changing, and changing rapidly enough that the original, small set-tlement knowledge base within the traditional utility organization is no longer effective. In fact, it outright fails. Now different entities are responsible for purchasing and delivering energy. The players involved can include generation companies, transmission compa-nies, distribution companies, inde-pendent system operators, power exchanges, retail energy suppliers, and metering agents—depending on jurisdiction.

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The resulting

effects on business processes and technology can be overwhelming to each and every one of these organi-zations: They have to enhance exist-ing processes and technology, or create and implement entirely new ones. While the purpose of this arti-cle is not to address the settlement and load profiling issues specific to each player in the utility industry, it is important to note the number of entities involved to demonstrate the complexity of this process.

sumes, and settling the amount of energy scheduled, generated, and delivered by the suppliers.

•

Financial/cost settlement

is the method of calculating the fees and charges related to energy procurement/delivery and the trading of funds among the market participants.

•

Wholesale settlement

comprises the processes between the ISO or transmission operator (e.g., where the distribution company releases product from the grid or metered interconnection point, a daily pro-cess based on hourly market prices, or half-hour prices in the United Kingdom).

•

Retail settlement

is a monthly process that occurs once actual metered consumption is acquired. It covers the processes from the distribution company down through the end-use customer.

Too many times, settlement is simplified to include just the exchange of funds and charges associated with the generation, transmission, and distribution of energy. But as shown above, that is only one-quarter of this complex process. It is for this reason—because the process is complex—that this article addresses only energy/load settlement, with a heavy concentration on the func-tion of load profiling.

II. Load Profiling: Making or Breaking Energy/Load Settlement

A load profile is the “estimated use of energy by a customer or group of customers for each hour of the day.”

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Since electricity can-

Settlement is simplifiedto include exchange

of funds and chargesassociated with

generation, transmis-

sion, and distribution.

This article uses META Group’s definition of settlement: “the cost accounting, assignment, and bill-ing for energy products and ser-vices consumed or exchanged in the marketplace.” The entire settle-ment process then breaks down into four categories: energy/load settlement, financial/cost settle-ment, wholesale settlement, and retail settlement.

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The definition is a good one because it is compre-hensive, taking into consideration the whole host of issues associated with the settlement process:

•

Energy/load settlement

is the method of obtaining or estimating how much energy a customer con-

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not be stored, the energy supplier must estimate the demand in advance in order to determine the generation, transmission, and distribution requirements. It is true that load profiling will make or break effective settle-ment, and that its effects on the other settlement categories are wide-reaching.

A simple example lies with the energy service provider (ESP) who depends on accurate load profiles to estimate the amount of energy its customers will con-sume. To finish out the energy settlement process, the ESP totals all of its customers’ estimates for the day by the house, and pur-chases wholesale power to reach these estimates. If in any hour actual consumption falls outside of the error-range specified by the LDC and approved by regula-tors (either below or above), pen-alties are assessed. It is crucial for ESPs to accurately estimate load profiles. This function directly impacts profitability, especially in an ever-changing market where hourly price spikes can be significant.

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raditionally, customers located in a certain geo-

graphic region were only served by their local utility. Thus, there was no need to allocate the load responsibility “beyond the level of the bulk delivery point meter for the area.”

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The single retail supplier (the local utility) was entirely responsible for managing this task. Deregulation has changed the way utilities manage load profiling. One of the chal-lenges facing utilities is under-

standing and complying with the rules mandated by local utilities for the supply of energy onto their systems.

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Suppliers can be sub-jected to hefty imbalance charges when the hourly demand placed by customers on a system devi-ates (within certain tolerances) from the actual resources the sup-plier has delivered to the system. In order to avoid these imbalance charges, energy suppliers must understand how different load

importance of other functions such as load profiling in a utility’s overall Retail Choice transition plan. Without a solid load profil-ing strategy in place, no other aspects of settlement can be suc-cessful. Consider the impacts of settlement on those entities exchanging energy. When load profile information is effectively packaged, energy buyers and sellers are better able to meet the demands of the new, deregu-lated market. Energy can be pur-chased and sold more efficiently and profitably, and utilities can better manage their facilities and energy usage.

oad profiling has a significant impact on customers as well.

Both print and electronic copies of proprietary load profile reports are available to energy customers. Load profiles can also be submitted electronically to energy suppliers and brokers, helping them to align supply needs with the best energy options. By profiling energy con-sumption, customers can negotiate power purchases and adjust energy user patterns to “take advantage of cost-saving tariffs and identify energy-wasting equipment for priority replacement.”

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III. Understanding the Nature of Load Profiles

Although load profiling is not a new concept, the advent of dereg-ulation has added new challenges to an established process. Local utilities routinely profile

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the characteristic energy consump-tion patterns of different types of customers and use this informa-

The advent of deregulation has added new challenges to an established process of loading

profiling.

profiling methodologies can affect the cost and accuracy of their fore-casted schedule. Should a sup-plier require tele-metering for all customers who want to partici-pate in Retail Choice? Even though historical interval

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data is the most accurate way to manage and predict customer load, some-times suppliers must depend on other load profiling approaches to predict load requirements for smaller customers who do not have access to hourly metering.

As mentioned above, much attention is given to the financial settlement process, and rightly so. But it must not overshadow the

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tion for many different purpose, such as rate analysis and design, cross point rate analysis for com-mercial and industrial (C&I) cli-ents, and revenue projections. The challenge for energy suppliers is deciding how best to use load profile data to accurately forecast customer

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demand on the LDC’s system. Consider for a moment that, as a supplier, the profiles you apply for scheduling, in most cases, come from outside your organization—typically from the LDC. Understanding the nature of these “supplied” profiles is criti-cal to the success of your opera-tions. You should be able so answer some basic questions about the profiles you use to cre-ate a schedule: What exactly does the profile represent (i.e., cus-tomer load class, customer type, etc.)? Was the profile created from historical interval data or was it derived using a formula? The information provided by these profiles gives suppliers and cus-tomers important data to make informed decisions to best man-age energy usage. By working together, energy suppliers and users can apply load profiling data to “ensure the right power is available at the most efficient price in an environment that reduces risk to both parties.”

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In a report to the DistribuTECH Europe DA/DSM Conference, the Load Research Group of the Elec-tricity Association Services, Ltd., set forth a set of general guidelines for load profiles:

•

Each profile should represent a relatively homogenous group of customers.

•

Each profile should be dis-tinctly different for the others.

•

The identifying characteris-tics for assigning customer load to a profile should be readily determined.

•

The number of load profiles should be relatively low.

•

The accuracy of estimated load profiles should be judged primarily on how well they per-form over a trading period (typically one year).

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such as load profiling. Recognizing the impact of load profiling on the deregulated market, the Maine Public Utilities Commission (PUC) set forth rules governing load obli-gations and settlement calculations for competitive electricity pro-viders operating in the state of Maine.

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Even though the rules focus on the “competitive electric-ity providers,” there is one particu-lar section aimed squarely at trans-mission and distribution (T&D) utilities. It requires that each T&D utility file a report describing “the methods by which the utility will create profiles from samples, esti-mate daily supplier loads, and esti-mate month-end energy differ-ence.”

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This is of significance because T&D utilities must ensure a reasonable level of accuracy when estimating a profile. It is then incumbent on the supplier to correctly apply the profile for load scheduling. The method by which the T&D utility produces the pro-file has a direct correlation to the accuracy of the profile and conse-quently on financial settlement.

B. General Profiling Methods

There are several effective load profiling methods available to the various participants in today’s energy market. When considering the optimal profiling method or methods for your organization, each should be considered in terms of cost, accuracy, and predictabil-ity. These three attributes vary sig-nificantly according to profiling method. The list below outlines the most common methods and is organized in decreasing order of cost and accuracy (predictability

Regulatory agencieshave taken steps toensure that market

participants givespecific consideration

to load profiling.

IV. Understanding Load Profile Creation

In addition to understanding characteristics of a good load pro-file, utilities must also understand the many methods used to pro-duce profiles. This section out-lines general load profiling meth-ods and explains the costs and benefits of each.

A. The Importance of Method: An Example from Maine

In many instances, regulatory agencies have taken early steps to ensure that market participants give specific consideration to areas

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will be addressed in the paragraph following).

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Dynamic metering or profiling.

A dynamic profile is produced when hourly load levels are assigned on a daily basis using interval metering for a sample of customers from each profile group. These hourly profiles reflect actual load based on actual conditions. Dynamic metering is the most accu-rate method, but it is also the most costly when you consider the finan-cial impact of installing interval meters (including the actual cost of each meter) and gathering data from these meters (either manually or via phone lines).

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Additionally, interval meters create interval data that now must be managed, creat-ing the need for costly personnel and information technology resources. It is important to note that although dynamic profiling uses hourly load levels, the profil-ing is not done in “real time.” It is still based on historical data.

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Dynamic modeling.

A dynamic model represents the cor-relation between load and an exter-nal factor such as weather. This type of modeling generally uses historical regression analysis of cus-tomer load as it relates to the weather that prevailed during the time of the load. The regression could be either a daily regression or an hourly regression for any season and day type of combination.

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In dynamic modeling, some part of the data is “derived” using formu-las, instead of being represented by the actual historical data.

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Proxy day (or “same day”) profiling.

Proxy day profiling is accomplished by selecting a day in

history

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that most closely matches the day being estimated. The proxy day can be chosen based on either system load or weather. Actual data from the sample for the selected proxy day is then used to create the profile.

•

Static profiling.

Static pro-files are typical-day representa-tions for any season and day type of combination. Static profiles do not reflect operating conditions of the day being estimated.

the scheduled energy amount with the actual energy consumed. The best method for creating and applying load profiles should strike a balance between cost, accu-racy, and predictability. A good model for this balance is seen in an example from Central Illinois Light Company (CILCO). At CILCO, the retail operation produces a consoli-dated schedule using a combina-tion of methodologies that corre-spond to the level of cost, accuracy, and predictability required for a very diverse group of clients, from schools to very large commercial customers. CILCO analysts employ everything from deemed profiles for street lighting, to dynamic profiling using sophisticated soft-ware tools. Some of these tools represent a significant investment for CILCO, but without them, CILCO would put significant revenue streams at risk.

C. Common Errors

As always, none of the methods are fail-proof. And of the three attributes (accuracy, predictability, and cost), it is accuracy that takes the biggest hit, as that attribute is most dramatically affected by errors during the profile creation and application processes. Accord-ing to Load Research Group, the most common errors are:

1. Profile Creation.

•

Sampling error.

However well designed a sample is, it will not replicate exactly and in all respects the load characteristics of the rele-vant population.

•

Modeling imprecision.

The statistical relationships designed to

Of three attributes (accuracy, predictability, and cost), it is accuracy that takes the

biggest hit.

•

Calendar rotation.

Calendar rotation is simply rotating a calen-dar of historical interval data to reflect the calendar of the time being estimated. Calendar rotation also does not reflect operating conditions of the day being estimated.

•

Deemed profiles.

Deemed profiles are engineering estimates and are typically used for very pre-dictable loads such as street lights or area lights. These will normally be based on type of light and num-ber of hours the light stays on.

The third factor, predictability, is also extremely important because financial settlement will reconcile

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identify the influence weather con-ditions have some limitations. Any modeling approach can only approximate reality.

•

Profile drift.

”Drift” associ-ated with sampling error is a par-ticular difficulty in load profile cre-ation where the population you are attempting to track changes rap-idly.

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For example, if a significant portion of the population you are tracking changes its consumption patterns by switching from elec-tricity to natural gas for heating, this could cause profile drift.

2. Profile Application.

The over-all process on applying the load profiles for energy trading and set-tlement purposes will also contrib-ute to the error rate. The main causes of profile application error are likely to be

•

errors in assigning customers to load profiles,

•

errors in estimates of suppli-ers’ consumption,

•

imperfections of the “algorith-mic profiling” method, and

•

the existence of customers who are not members of the popu-lations represented by existing load profiles.

The bottom line is that there is ample chance for error when creat-ing and applying profiles as part of your business process. Being aware of the most common instances may help you avoid some of the com-mon pitfalls. Taken individually and on a small scale, these errors may slip through the cracks. But as customer size increases and aggre-gate load increases, the errors are magnified exponentially. At these aggregate levels, the effect can be devastating.

V. Looking Ahead: A Future Destined for Automated Meter Reading?

Despite the numerous benefits of load profiling—when performed correctly and accurately—to the load settlement process, it is clear that interval metering or automated meter reading (AMR) offers a more accurate and long-term solution. As a result, much has been written lately about AMR and its crucial role in a retail choice environment. While there is no question that interval metering allows the most accurate and timely customer response to generation prices, it may not be immediately feasible for small customers to absorb the costs associated with the installation and use of interval meters. Load profil-ing is one option that will help these customers gain access to the competitive generation market. It may be more accurate to say that load profiling is a good transition strategy rather than a strategic plan. As new technologies mature and the cost of advanced metering devices begin to drop, customers will accept that the benefits of investing in metering hardware and software without question outweigh the costs.

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Endnotes:

1.

Karen Edge,

Settling in an Unsettled World

, META Group, File 130, Feb. 17, 2000.

2.

Id.

3.

Id.

4.

Id.

5.

Edision Electric Institute, Uniform Business Practices Interim Report (pre-pared by Wayfinder Group), Mar. 10, 2000, Section VI, at 54.

6.

These rules will normally be part of the LDC’s Open Access Transmission Tariffs.

7.

Interval data is metered load data gathered remotely, no less frequently than daily.

8.

James B. Halpern,

Information Fuels Energy Management

,

Elec. Light & Power

, Feb. 1999, at 19, 23.

9.

More specifically, a pattern of electric-ity demand for a customer, or a group of customers, over a given period. Profiles are normally measured at half-hour intervals across a day, and also through the course of the year.

10.

In this case,

customer

refers to accounts traditionally served by the LDC that are now taking service from another provider as part of a retail choice program.

11.

Halpern,

supra

note 8.

12.

S.V. Alvera and A.G. Horsburgh,

Load Profiling for Energy Trading and Settlement in the U.K. Electricity Markets

, presenta-tion report from Distribu-TECH Europe DA/DSM Conference, Oct. 1998.

13.

Maine Public Utilities Commission Rules (effective Dec. 28, 1999), Part 3, Chapter 321.

14.

Id.

, Ch. 321, Section 9, “Reporting.”

15.

Maine Public Utilities Commission,

Public Utilities Reports Guide

, Ch. 31, “Conservation, Load Management and Demand-Side Management,” 1999.

16.

In states where open access is being rolled out by customer type (i.e., C&I cus-tomers first), virtually all customers already have telemetering devices installed. In these cases, the customers do not incur additional expenses for installa-tion of these devices. This will change as the market opens up to smaller commer-cial and residential customers.

17.

In some cases, utility companies will perform the regression analysis for the suppliers. The suppliers provide the aggregated load, and the utility uses existing systems and processes to run the regressions.

18.

The historical day may be the same day last year, month, or even week.

19.

Such as adding or subtracting a large number of meters in a short period of time, or having customers within the group switching to interval metering.

load profiling for retail choice: examining a complex and crucial component of settlement

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