1148 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 21, NO. 3, AUGUST 2006

Establishing Quality Performance of DistributionCompanies Based on Yardstick Regulation

J. E. Pinheiro dos Santos Tanure, Member, IEEE, Carlos Mrcio Vieira Tahan, andJ. W. Marangon Lima, Senior Member, IEEE

AbstractThis paper proposes a procedure and methodology forperformance target setting related to continuity metrics in elec-tricity distribution networks. This proposal deals with one of thekey issues in the monopoly regulation, which is either the reduc-tion of the information asymmetry among economic agents or theemulation of a competitive environment in industry segments usu-ally considered as natural monopolies. This paper develops an ap-proach toward standard definitions for Customer Average Inter-ruption Duration Index (DEC) and Customer Average Interrup-tion Frequency Index (FEC) based on inter-companies compara-tive analysis.

This approach introduces productive efficiency concepts to thedefinition of network performance. In order to do so, the dataenvelopment analysis (DEA) technique, used for defining the effi-ciency frontier, was combined with the dynamic cluster technique,oriented toward models of identification of similar networks. Atechnique for comparative analysis was used for the definition ofthe network expected performance. Examples with the Brazilianelectric systems are provided to clarify the methodology suggestedby this paper.

Index TermsData envelopment analysis (DEA), distributionreliability, dynamic cluster analysis, quality performance, yard-stick regulation.

I. INTRODUCTION

THE challenge of optimizing the use of electrical sys-tems must be considered as a constant task both for theproviders, once it maximizes their profits, and the RegulatingCommission, as an attempt to extend part of the benefits in-ternalized by the providers to the society. This paper proposesa methodology aiming at establishing performance goals forthe distributors, allowing the Regulating Commission to definethe Customer Average Duration Interruption Index (DEC)and Customer Average Frequency Interruption Index (FEC)continuity index standards for the distribution companies in anenvironment where asymmetry of information is remarkable.

This paper uses two data analyzing techniques. One is relatedto network classification techniques, introducing a dynamiccluster technique when classifying sets of consuming units.The other one uses a comparative performance analysis calleddata envelopment analysis (DEA) for establishing qualityparameters regarding the distribution network.

Manuscript received August 22, 2005; revised March 23, 2006. This workwas supported in part by CNPq. Paper no. TPWRS-00531-2005.

J. E. P. S. Tanure is with the University of Salvador, Salvador, Brazil.C. M. V. Tahan is with the University of So Paulo, So Paulo, Brazil.J. W. Marangon Lima is with the Federal University of Itajub, Itajub, Brazil.Digital Object Identifier 10.1109/TPWRS.2006.879283

In the suggested methodology, both techniques are used.Initially the new alternative classification, the dynamic clustertechnique, aims at identifying similar networks that composethe sets of consuming units. The DEA technique is then appliedto each formed cluster. The elements to be compared usingDEA technique are the sets that belong to the cluster obtainedpreviously. In order to use the DEA technique, it is necessary todefine the parameters that compose the inputs of the produc-tion units, which represent the continuity of electrical energysupply through the energy network. Likewise, the outputs orproducts are the reliability continuity indexes. This procedureprovides a way to establish the parameters that are necessary tocompare the performance of the distribution companies.

Examples with the Brazilian electric systems are provided toclarify the methodology suggested by this paper.

II. REGULATING THE DISTRIBUTION SERVICECONTINUITY IN BRAZIL

In Brazil, the continuity of the distribution service is mea-sured by using two sets of indicators: individual and collectiveindicators. The DEC and FEC are collective indicators and areused in this paper:

DEC (1)

FEC (2)

wherenumber of consuming units that suffer from aninterruption from an event ;duration of event ;total number of events;total number of consuming units of set ofconsumers.

They are very similar to the well-known SAIDI and SAIFI,respectively [1]. Aiming at standardizing the indicators as toolsthat can be used for collecting, treating, and distributing data re-garding service continuity, ANEEL, the Brazilian national reg-ulating commission, enacted Resolution 024/2000 [2], incorpo-rating the methodological advances of the regulations and con-cessions previously signed [3]. Based on the standardization ob-tained from this resolution, the conditions to use the compara-tive analysis proposed in this paper were established among dis-tributors.

0885-8950/$20.00 2006 IEEE

TANURE et al.: ESTABLISHING QUALITY PERFORMANCE OF DISTRIBUTION COMPANIES 1149

Resolution 024/2000 established the necessary conditions tocompare the performance of the distribution companies. Thisresolution created the quality performance analysis based on di-visions of the concession area named sets. Millions of sets werethen created, and the comparisons, which were primary donecompany by company, changed to set by set. The central idea isthat the sets are better comparable instead of the company itselfwhere the performance average did not allow the same treatmentto all consumers with the same characteristics. Based on the sets,which can be more homogeneous after a clustering process, itis possible to address the performance goals in a more fair way.This resolution describes the process of data collection, clus-tering, and the establishment of the goals. Although in the be-ginning there were many complaints of the distribution compa-nies about the complexity of the quality regulation introduced,now there is a common sense that this was necessary for the sakeof transparency of the whole process and, consequently, for di-minishing the discretionary power of the regulatory board.

These indicators are gathered by a set of consumers pooledfrom the areas that form the subsets of distribution concessions[4]. The Brazilian regulator has a database of approximately6000 sets all over the country, representing the distributors par-titions, which allowed comparative analyses based on a signif-icantly large database. Within context, it becomes possible toperform this sort of comparative analyses, in some cases, evenfor a single company. Notice that one distribution company mayhave many sets of consuming units with different performance.

It is important to mention that from the customer point ofview, the interruptions are the distribution companys respon-sibility, no matter the origin of the fault. However, if the regu-latory agency is comparing the performance of the sets, the ex-ternal interruptions need not be included in the DEC and FECindexes.

III. SUGGESTED METHODOLOGY

The idea for implementing a comparative analysis lies oncombining the two techniques: dynamic clusters [5] and dataenvelopment analysis [6]. This combination allows one to iden-tify similar sets, or blueprints, by using a new grouping systemas an alternative to the traditional non-hierarchical cluster tech-nique, which is currently used by ANEEL. After this identifica-tion comes a comparative analysis in order to identify the ele-ments that offer a better performance in each cluster [7]. A sim-ilar approach using DEA and cluster analysis together is foundin [8] for urban public transportation but using cluster analysisjust to explain the results of DEA technique, not as a pre-clas-sification purpose.

A. Methodological Approach Overview

Distribution network is usually built to meet pre-defined per-formance standards and its dimension is established by meansof a reliability study [1]. Normally defined on historical data ob-tained by the company, these standards carry management prac-tices that can be subjected to improvement on every aspect. Net-work maintenance practices, crew training, and network recon-figuration are among the techniques and procedures that couldalso be improved.

TABLE IPOSSIBLE PARAMETERS TO BE USED IN COMPARATIVE ANALYSIS

In order to stimulate continuous performance improvementwithin distribution companies, a comparative analysis amongthem has proven to be an ideal way to obtain the bases for anefficient regulation. Particularly in the energy sector, where thedistribution segment is characterized by a monopolistic activity,these analyses are extremely relevant.

In terms of quality indicators, the need to establish perfor-mance standards for distribution networks places the regulatorycommission in an environment where there is considerable in-formation asymmetry, for each distributor only carries informa-tion regarding its own performance possibilities.

In this environment, the regulatory commission needs torely on tools that allow the reduction of such asymmetry,contributing to the determination of better and more appro-priate quality goals for distributors. It is important to say thatunderestimated goals may lead to a larger profit margin forthe distributors because networks will receive fewer invest-ments, whereas unnecessarily overestimated goals will lead toa greater investment in networks, which in turn will lead togreater pressure toward raising the cost of energy.

This way, network elements can be seen as inputs to comeup with a determined quality standard for distribution systems.This paper will try, as much as possible, to treat the existingnetworks as producing units that bear the following elements:

inputs: installed capacity, network extension, maneuver el-ements, among others;

outputs: service quality measured by the DEC and the FEC.Initially, one must consider that each set (a subset of the con-

cession area) represents a producing unit. This set is usually acity, a neighborhood, a rural area, etc., depending on the char-acteristics and importance they were given by the distributor.These sets are characterized by an area that will be served, anumber of existing consumers, and a consumption standard as-sociated to these consumers. Based on these attributes, whichare not subjected to the distributors control, it is possible toidentify similar sets, which will probably demand similar net-work configurations. Taking these sets as defining elements of aproducing unit, the use of determined quantitative network ele-ments must be associated to certain performance standards.

This way, the methodology proposed here uses the clusteranalysis technique to establish which sets are similar. The pa-rameters or attributes used for carrying out such classificationare described in Table I. The number of consumers, the totalload, and the area are the attributes used in this first stage.

It is important to mention that the parameters considered forclassifying the sets are not control variables for the companies.Therefore, they need to adjust the inputs variables to attend theconsumers with a pre-specified quality.

1150 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 21, NO. 3, AUGUST 2006

Once similar producing units are defined, i.e., the sets thatbelong to the same cluster, the efficiency frontier identificationtechnique, DEA, is used for classifying them according to theirdistances to these frontiers. In this second stage, data of the dis-tribution network are considered as inputs (see Table I). More-over, the operation and maintenance cost (O&M) are also in-cluded because they reflect how the companies should operatetheir network.

In this case, the input is the regulated O&M derived from amodel company adjusted to the distribution profile. This modelcompany is used in the tariff revising process in Brazil. Theexplanatory variables usually adopted to define this theoreticalcompany are the number of consumers, the extension of the con-cession area, number of posts, and the average distance betweenthem. As a result, this company is then expressed in terms of thenumber of employees, number of vehicles, maintenance costs,and administrative costs. The number of employees and vehi-cles are also transformed into costs using the average salary andthe car leasing costs of the region where the company is located.

These two techniques combined allow the regulator to estab-lish performance goals for the existing sets by using the bestpractices of the existing distributors as a reference. More detailsof these techniques and their application to quality regulationare described hereafter.

B. Dynamic ClusterThe dynamic cluster technique [5] was used for defining the

similar sets, and it was implemented according to the followingsteps:Step 1) Each element is considered as the geometric center

of the cluster to be created.Step 2) Having defined the center, the degree of similarity is

determined for all the elements.Step 3) A selecting criterion is established for the degree of

similarity between the center and each element.Step 4) For each center, the most representative elements are

grouped based on their similarity.Step 5) The main characteristics are then taken from the sets

formed.Step 6) The whole process is repeated for each element.

The criterion used for defining the degree of similarity isthe Euclidian distance measured by the attributes. Before cal-culating this distance, a normalization procedure is carried outfor each attribute. The Gauss curve is regularly used for definingthe parameters for the normalization.

This algorithm allows the attainment of as many clusters asthere are elements being analyzed, thus making it possible to es-tablish several selecting factors. For the suggested application,the selecting criterion must provide a minimum number of el-ements necessary to perform the DEA efficiency frontier anal-ysis, also observing the degree of dispersion among the elementsof the formed sets. This restrictive criterion guarantees that theanalyzed set will be formed by its most similar elements withinthe studied universe.

C. Data Envelopment AnalysisDEA has become a practicable approach to evaluate the rel-

ative efficiencies of decision-making units (DMUs) in various

contexts. The DEA approach, called CharnesCooperRhodes(CCR) model, was first introduced by Charnes et al. [6] to pro-duce an efficiency frontier based on the concept of Pareto op-timum. The DEA has been used with efficiency measuring pur-poses in various entities in the public and private sectors. Asfar as distributing companies are concerned, this technique isused to measure the relative efficiency of: service centers [7];distribution utilities themselves [9]; and the distribution addedvalue [10]. The last two ones deal with the utility as a whole, i.e.,each utility represents a producing unit or a DMU. In this paper,a classification stage is derived first in order to guarantee thatonly homogeneous units are compared. This pre-classificationis mainly necessary when continental countries like Brazil areconsidered, where diversity in service rendering can be found,even inside the concession area of a single distribution com-pany. However, the main problem of splitting the company intoconsuming sets or sets of consumers is related to the data. TheBrazilian regulator has successfully acquired the attributes de-scribed in Table I from the utilities, but a lot of work has yet tobe done.

Therefore, the sets of consumer units and the distributionsystem associated are the DMUs. The DEA technique is thenapplied to these producing units within the cluster defined inthe classification stage, where the dynamic cluster technique isapplied. The formulation is oriented to maximize production,according to optimization problem shown in the following:

subject to

(3)

where is the efficiency index for the DMU ,are the inputs (for instance: line_km,

transformer installed capacity, etc.), are thes outputs (DEC and FEC), and are the weighting factorsthat allow the convex combination of inputs and outputs forthe DMUs (in this paper, sets of consumer units with theassociated distribution networks).

The lambdas can be better interpreted as the multipliersthat express how much it is needed to increase or decrease theinputs or goods of one particular unit production to reach themost efficient unit of production. This is the main idea of DEA,i.e., trying to get the efficient frontier of a group of sets. Afterthis identification, the multipliers to be applied to the variablesin analysis are obtained, and then we find the values of the vari-ables that equal the performance of this particular set to the ef-ficient one.

This optimization problem (3) is a standard DEA formulation[11], and in our case, the interest lies on the output efficiencyrather than on the input efficiency, because there is no possibilityof deactivating part of the existing distribution network.

TANURE et al.: ESTABLISHING QUALITY PERFORMANCE OF DISTRIBUTION COMPANIES 1151

Equation (3) is then solved for each cluster element or DMU,giving an associated value. The element that reaches thevalue is the most efficient in the group. The remainingelements present higher values, meaning that they have an ef-ficiency of in relation to the benchmark. This relation isuseful for the regulator to define performance goals for the setsof a given cluster.

There is, however, the need to adapt this general model for thespecific case studied here. Normally, given a fixed input quan-tity, the desired number represents the maximum possible out-puts. In terms of quality, and especially with the DEC and theFEC, which are used as outputs in this paper, the elements thatobtain the lowest values rather than the highest ones show abetter performance. In order to adapt these indexes to the DEAgeneral model, their output vector must undergo a transforma-tion

(4)

where

adjusted quality index;maximum value of the quality index related tothe cluster;value to be adjusted;minimum value of .

The vector formed by the values of becomes the newoutput vector according to the general formulation. The valueis chosen from the degree of sensitivity to be inserted in theproblem. If is too large, the sensibility among the sets would belower, and if the is zero, it would have a null . After someattempts considering the Brazilian case, the value of one hourshowed good results for the DEC. Other types of transformationinstead of (4) may also be used, but this one already presentedgood results. A detailed study was performed in [12].

D. Inputs, Outputs, and GoalsThe definition of performance goals can be formulated based

on many attributes, such as the ones shown in Table I. In spite ofthe large number of attributes whose use is feasible in this sortof analysis, determining them becomes a major problem oncethe distributors are demanded to perform a vast survey whenforming components of their networks, which does not alwaystake place.

As far as Brazil is concerned, the regulator, ANEEL, an-nually collects the following data for each set of consumers:area, primary network extension, installed capacity, number ofconsumers, and consumption. In addition, the analysis includedthe costs regarding the operations and maintenance of eachcompany recognized by ANEEL in the process of tariff review.These costs are distributed among the sets, considering they arecorrelated with the network extension.

All of the process can be summarized as follows.First, a classification of the sets is carried out. It is important

to bear in mind the need to identify the similar elements basedon the classification attributes. As the first column in Table Ishows, no internal aspect regarding the distributor is considered

TABLE IICOMPANY EFFICIENCY (%)

in this classification, which means that only the information andcharacteristics regarding the clients are taken into consideration.

Second, for each cluster, the DEA technique is used for iden-tifying the efficiency frontier and the relative distance of the el-ements that belong to this cluster. Here, the inputs and outputsrepresent internal characteristics of the distributors, displayed inthe second and third columns of Table I.

Based on the efficiency attributed to each element , andconsidering the relation input/output, the definition of the per-formance goals of this element can be obtained. In general, theANEEL is proposing that these goals are to be achieved withintwo periods of tariff review, which means an eight-year timespan average. This process is then repeated for each tariff re-view. This definition and treatment involve setting decreasinggoals for every element. Therefore, the distribution company,which is far from the efficiency frontier, has enough time toadapt itself to the new target.

IV. CASE STUDYIn order to exemplify the suggested methodology, a case

study was initially developed relying on the help of the sevenmain distributing companies in Brazil, i.e., CEMIG, CELESC,COPEL, EBE, ELETROPAULO, COPEL, and ELEKTRO. Inthis analysis, 924 sets of consumer units from these companieswere studied. The first analysis can be seen as the traditionalDEA analysis, where the DMUs represent the companies asa whole. It shows the direct application of DEA to establishquality performance. The second and the third analyses showthe application of the proposed method, where the DMUsrepresent the sets of consumer units.

A. Company-Oriented AnalysisIn this analysis, each company was considered as a producing

unit, i.e., the classification stage of the sets of consumer unitswas skipped. These companies were considered as parts of a ho-mogeneous group. Observe that this approach is similar to thoseused in [9] and [10], but the difference is that in this paper, thequality performance is the output variable. The results obtainedare listed in Table II. The values for DEC and FEC are globalaverage values for each company. The relevance of the inputgroup was also evaluated in different ways. For instance, in thesecond column, three inputs were used: capacity of transforma-tion (MVA), network extension (Km), and O&M. All these at-tributes underwent a normalization process. The values obtainedrepresent the efficiency in percentage.

1152 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 21, NO. 3, AUGUST 2006

The number of elements to be analyzed must be at least two tothree times the number of inputs. This constraint is a guaranteethat all elements will not be at the efficiency frontier, i.e., theywill not define this frontier [6], [11].

From the results obtained, the following are clear. Regarding all considerations on inputs, CELESC seems to

be the company positioned on the efficiency frontier. EBE could be taken as the second most efficient among the

analyzed companies, regardless of the input evaluated. In the simulated scenarios, CEMIG shows the greatest in-

sensitivity regarding the kind of input used, that is, its rela-tive efficiency does not vary according to the change in theinput variables.

COPEL has proven to be extremely sensitive to O&Mcosts. Its O&M management might be fundamental forits overall efficiency. The absence of such input arouses asignificant variation in its performance, from 146.0% to304.99%.

ELETROPAULO, on the other hand, showed a strong sen-sitivity toward the input network extension. This can beexplained because the ELETROPAULO concession area islocated at the highest density load of the country. If thisinput variable is not taken into account, its performancedeteriorates from 161.78% to 599,15%.

CPFL shows strong sensitivity toward the parameters net-work expansion and installed capacity. These inputs aremost relevant to define the degree of efficiency. Neverthe-less, their results are relatively stable within the analyzedset, allowing the assumption that the management of thethree factors is well balanced. In all cases, their score variedbetween 200% and 300%.

One of the crucial points regarding the use of the DEA is theinclusion of scale economy. In this analysis, there was no con-sideration in relation to the scale of the considered producingunits. Therefore, their results are only good to evaluate the rel-ative efficiency regarding the use of each input. Out of these re-sults, three inputs were chosen to analyze the sets, that is, onlythe ones listed in Table I.

B. Set AnalysisAmong the 924 elements, which belong to the previously

mentioned companies, three elements or sets of consumers wereselected to exemplify the definition of the DEC and FEC goals.The chosen elements were: ELETROPAULOs Airport area;CELESCs Alfredo Wagner area; and EBEs Taubat area. Inorder to determine the most similar elements, each one becamethe geometric center of a cluster specifically adjusted for thispurpose. Given that there are three inputs, the nine most similarelements within the considered universe were found. Then, thedegree of efficiency of this element was obtained when it wascompared with the other elements of this cluster.

As an example, Table III presents the values calculated forthe Airport set/element, as well as for the other elements thatbelong to the Airport cluster. The performance goals differ foreach element of this cluster because of the difference on theefficiency. In this particular case, all the elements belong to thesame company, showing that the analysis carried out on item Ahas drawbacks because of the internal diversity.

TABLE IIICLUSTER WHERE AIRPORT AREA IS THE GEOMETRIC CENTER

TABLE IVRESULTS OF THE SET ANALYSIS

TABLE VRESULTS FROM THE INTERTEMPORAL ANALYSIS BY SET

From this table, Sao Paulo Centro and Sto Andre are at theefficiency frontier and, therefore, are the benchmark for the Air-port element, which can improve its performance in about 84%.

Table IV shows the results attained for the three elements,including the Airport area.

Comparing the current DEC and FEC values of the three el-ements with their established goals by using the methodologyproposed by this paper, it is possible to see that the attainedresults are perfectly consistent with the values in use, i.e., therecommended goals for the fourth year are close to the currentvalues. One can also see that the Taubat set already presentshigh efficiency; the proposed goals (6.58) are already close tothe current one (7.00).

This additional verification was made for all the elements.The results attained were consistent.

C. Set Analysis Considering Two-Year SampleIn order to stabilize the analysis, a sample period of two years

was established in contrast with one year performed in item B.Therefore, the analysis base was broadened considering the el-ements resulting from the sets classified for two consecutiveyears. Table V shows the results obtained in this case.

As it was verified, the attained results are still consistent withthe values in use by ANEEL. The Taubat set is now consideredto be the efficiency frontier element. For this matter, no newquality goals were assigned for this set.

V. CONCLUSIONSThe use of a comparative analysis among companies is an ex-

tremely useful tool in the relation between the regulator and the

TANURE et al.: ESTABLISHING QUALITY PERFORMANCE OF DISTRIBUTION COMPANIES 1153

regulated companies. Nevertheless, the analytical techniques,which support this analysis, depend on a massive amount ofgood quality information and data. Most of the time, these datadepend on surveys carried out by the company itself, which mayweaken the reliability of the results.

In the case above described, a large amount of sets formedin each company practically impairs the manipulation of thesedata. In Brazil, there are 64 distribution companies and approx-imately 6000 sets of consuming units, which constitute a con-siderably large database to be analyzed.

The combination of these two techniques dynamic clusteranalysis and data envelopment analysis has proven to be fairlyrobust, and it is able to minimize the information asymmetryeffect between the involved agents in the definition of qualitygoals. Furthermore, it is relevant to observe that the final resultcannot be taken as an absolute truth. The goals attained must beseen as guiding values.

It is important to mention that the suggested methodology sig-nificantly reduces the degree of subjectivity of the regulator inits mission to establish performance standards for distributioncompanies. This fact makes this kind of regulation better ac-cepted by the agents, for it comes from a methodic and objectiveprocess, which is also reproducible and fair.

The proposed technique regarding the establishment ofquality goals is under consideration by the Brazilian Regulator,ANEEL, as an advance in the current methodology.

ACKNOWLEDGMENTThe authors would like to thank ANEEL for all the data pro-

vided for this study.

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[3] ANEEL (200), ANEEL Resolution 24. [Online]. Available:http://www. aneel.gov.br.

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[5] D. Grigoras, R. McInernya, and C. Mulcahy, MAISthe mobileagents information system support for creating dynamic clusters,Proc. IEEE 5th Int. Conf. Algorithms Architectures Parallel Pro-cessing, 2002.

[6] A. Charnes, W. W. Cooper, and E. Rhodes, Measuring the efficiencyof decision making units, Eur. J. Oper. Res., vol. 2, no. 6, pp. 429444,1978.

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[9] A. Pahwa, X. Feng, and D. Lubkeman, Performance evaluation ofelectric distribution utilities based on data envelopment analysis, IEEETrans. Power Syst., vol. 18, no. 1, pp. 400405, Feb. 2003.

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J. E. Pinheiro dos Santos Tanure (M00) received the B.Sc. degree from theFederal University of Bahia, Salvador, Brazil, in 1980, the M.Sc. degree fromthe Federal University of Itajub, Itajub, Brazil, in 2004, and the D.Sc. degreefrom the University of So Paulo, So Paulo, Brazil, in 2004.

He was with Companhia de Eletricidade do Estado da BahiaCOELBA, thedistribution company at the State of Bahia. From 1998 to 2002, he was withANEEL, the Brazilian National Regulatory Agency, as a Superintendent of Dis-tribution Service Regulation. In 2003, he was also with the Ministry of Mine andEnergy as a member of the group that elaborated the New Brazilian ElectricityModel. He is now an Associate Professor at University of Salvador, Salvador,Brazil.

Carlos Mrcio Vieira Tahan received the B.Sc., M.Sc., and D.Sc. degrees fromthe University of So Paulo, So Paulo, Brazil, in 1971, 1979, and 1991, respec-tively.

From 1971 to 1992, he was with Themag Engenharia, where he developedstudies on transmission and distribution. In 2000 to 2003, he was with the PublicServices Commission of So Paulo State as a Commissioner. He has been aLecturer at the University of So Paulo since 1989.

J. W. Marangon Lima (SM06) received the B.Sc. degree from the MilitaryInstitute of Engineering, Rio de Janeiro, Brazil, in 1979, the M.Sc. degree fromthe Federal University of Itajub, Itajub, Brazil, in 1991, and the D.Sc. degreefrom the Federal University of Rio de Janeiro in 1994.

From 1980 to 1993, he was with Eletrobrs, the Brazilian holding companyfor the power sector. Since 1993, he has been with the Federal University ofItajub as a Professor of electrical engineering. In 1998 to 1999, he was alsowith ANEEL, the Brazilian National Regulatory Agency, as a Director Advisor.In 2003, he was also with the Ministry of Mine and Energy as a member of thegroup that elaborated the New Brazilian Electricity Model. He is currently inhis sabbatical year in the Operations Research Department, University of Texasat Austin.