AHP Decision-Making Algorithm to Allocate Remotely Controlled Switches in Distribution Networks

1884 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 26, NO. 3, JULY 2011

AHP Decision-Making Algorithm to AllocateRemotely Controlled Switches in

Distribution NetworksDaniel Pinheiro Bernardon, Mauricio Sperandio, Vinícius Jacques Garcia, Luciane Neves Canha,

Alzenira da Rosa Abaide, and Eric Fernando Boeck Daza

Abstract—Continuity in power supply for the consumers is a per-manent concern from the utilities, pursued with the developmentof technological solutions in order to improve the performance ofnetwork restoration conditions. Using remotely controlled switchescorresponds to one possible approach to reach such an improve-ment and gives some convenient remote resources, such as the faultdetect, isolation, and transfer loads. This paper presents a method-ology implemented in a computer programming language for al-location of these devices in electric distribution systems based onthe analytic hierarchical process (AHP) method. The main con-tributions focus on considering the impact of installing remotelycontrolled switches in the reliability indices and the AHP decision-making algorithm for the switches allocation. The effectiveness ofthe proposed algorithm is demonstrated with case studies involvingactual systems of the AES Sul utility located in Southern Brazil.

Index Terms—Analytic hierarchical process (AHP) decisionmaking, distribution networks, logical-structural matrix, relia-bility, remote-controlled switches.

I. INTRODUCTION

U TILITIES have concentrated on significant efforts to im-prove the continuity of the supplied electrical energy, es-

pecially due to regulatory policies, besides customer satisfactionand improving the amount of energy available to commercialand industrial activities.

Moreover, supply interruptions are inevitable due to the im-plementation of the expansion of the system, preventive main-tenance on network components, or even by the action of pro-tective devices due to defects [1].

When facing a contingency situation, to act as soon as pos-sible may result in a minimally affected area. Whenever a fault is

Manuscript received October 01, 2010; revised January 12, 2011; acceptedFebruary 20, 2011. Date of publication April 07, 2011; date of current versionJune 24, 2011. This work was supported in part by AES Sul Distribuidora Gaúchade Energia SA, in part by Conselho Nacional de Desenvolvimento Científico eTecnológico (CNPq), in part by Fundação de Amparo à Pesquisa do Estado doRio Grande do Sul (FAPERGS), and in part by Coordenação de Aperfeiçoa-mento de Pessoal de Nível Superior (CAPES). Paper no. TPWRD-00755-2010.

D. P. Bernardon, M. Sperandio, and V. J. Garcia are with the Federal Univer-sity of Pampa, Algrete 97546-550, Brazil (e-mail: [email protected]; [email protected]; [email protected]).

L. N. Canha and A. Abaide are with the Federal University of Santa Maria,Santa Maria 97105-900, Brazil (e-mail: [email protected]; [email protected]).

E. F. B. Daza is with AES Sul Power Utility, São Leopoldo 93010-060(e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TPWRD.2011.2119498

identified at any point of the network, the following proceduresmust be performed: identifying the right defect position; iso-lating the part of the distribution system as much as possible byopening normally closed switches; restoring the power supplyto consumers downstream of the isolated block; correcting theproblem; and reoperating the switches to get back to the normalnetwork status.

Currently, a topic that is being frequently discussed is howelectric power distribution systems will be in the future. In thissense, the term “Smart Grid” was defined to describe how thisnew network should behave, that is in a “smart” or “intelli-gent” way. Among the features of a smart grid are the abilityto carry out maneuvers in an automated manner (self-reconfig-uration) and high reliability, all with low operation and mainte-nance costs. A survey of the main projects and research relatedto smart grid is presented in [2].

Automation of distribution systems plays an important roleon reducing the time to implement a service restoration planwith the installation of remote-controlled switches, mainly byallowing the consideration of regulation policies. These deviceshave shown to be economically viable due to the increase ofa large number of suppliers of automation equipment and newcommunication technologies [3].

The use of an effective methodology for allocating remote-controlled switches is really important for the utilities, since thatprocedure is closely related to the restoration time and conse-quently associated with the reliability index. This kind of so-lution is not easy to deal with due to its multicriteria, combi-natorial nature, and the difficult mathematical modeling. Moststudies that are directed to solve the problem of switch alloca-tion in distribution systems [4]–[8] do not deal with remote-con-trolled switches, which modifies the objective function signifi-cantly. The research of Cox [9] and Wagner [10] discusses thissubject, but it is limited to strategies for the operation of re-mote-controlled switches, without covering the allocation. Theresearch by Asr and Kazemi [11] considers the switches alloca-tion; however, a monocriteria approach is used, and the resultsare limited to small systems.

This paper deals with developing computer algorithms to ad-dress the remote-controlled switch allocation problem with mul-tiple objectives using an analytic hierarchical process (AHP)[12] in order to improve the reliability index of the distributionsystems. The AHP method has proven to be effective in solvingmulticriteria problems, involving many kinds of concerns, in-cluding planning, setting priorities, selecting the best among a

0885-8977/$26.00 © 2011 IEEE

BERNARDON et al.: AHP DECISION-MAKING ALGORITHM 1885

number of alternatives, and allocating resources. It was devel-oped to assist in making decisions where competing and/or con-flicting evaluation criteria exist [13].

Thus, the proposed algorithm can be configured accordingto the needs of the utilities, helping in the decision-makingprocess. The system will indicate where the resources investedby the utility will bring better operative results concerning theimprovement of the reliability index in distribution systems,being characterized as a decision support tool for planning andoperating the distribution networks.

The proposed approach has proven its effectiveness on a spe-cific portion of the AES Sul distribution system in the sense ofimprovement in the reliability index and in the reduction of crewdisplacements, letting one conclude the relevant economic andcustomer satisfaction benefits obtained.

Therefore, the main contributions of this paper are high-lighted as follows.

• A new method to calculate the impact on the reliabilityindex due to the installation of remote-controlled switches.

• A multicriteria decision-making process for solving the re-mote-controlled switch allocation problem using the AHPmethod.

In addition, the algorithms used for the calculation of the loadflow and the reliability indices are also presented, since they areessential for modelling the problem considering the analysis ofload transfers.

II. ALGORITHM OF LOAD FLOW

A version of the classical backward/forward sweep methodalgorithm was performed to calculate the load flow in radialdistribution networks developed by Kersting and Mendive [14].Since the electrical loads are defined by a constant behavior be-cause of the voltage applied, this results in an unusual solutionfor calculating the load flow, since the current absorbed by theloads depends on the voltage, and this value is unknown. Thisway, the solution is found only iteratively. The resulting proce-dure is described as follows.

1st Stage: It is considered that the voltage in all points of thefeeder is the same as the voltage measured in the substation bar.This information can be automatically received by the remotemeasurement systems installed at the substations. Do not con-sider voltage drops in the branches at this stage.

2nd Stage: Active and reactive components of the primarycurrents absorbed and/or injected in the system by the electricalelements are calculated.

3rd Stage: The procedure to obtain the current in all networkbranches consists of two steps: 1) a search in the node set isperformed by adding the current values in the set of branchesand 2) currents from the final sections up to the substation areaccumulated.

4th Stage: Voltage drops in primary conductors are deter-mined.

5th Stage: From the substation bar, it is possible to obtainthe voltage drops accumulated at any other part of the primarynetwork, and, consequently, the voltage values at any point.

6th Stage: The difference between the new voltage values forall nodes and the previous values is checked. If this differenceis small enough, the solution for the load-flow calculation was

found and the system is said to be convergent. Otherwise, theprevious steps are repeated, from step 2 and on, by using thecalculated voltages to obtain the current values. Iterations areperformed until the found difference is lower than a threshold.In this paper, a threshold of 1% was chosen, because it promotesaccurate values for the status variables without requiring toomuch time to process.

At the end of the process, the active and reactive powers andthe technical losses in the primary conductors are defined for allfeeder branches.

This load-flow method was implemented in the proposedmethodology for analyzing the technical feasibility of the loadtransfers, that will influence in the points applied to obtainthe allocations of the remote-controlled switches. The criteriarelated to the technical feasibility of load transfer through theremotely controlled switches were considered constraints. Thatis, these transfers may not result in overloading the electricalelements, violating the permissible limits of the protectivedevices and do not violate the permissible voltage range limitsof the primary networks. The checking of the constraints is per-formed considering the maximum load profile by representingproperly the most severe operation scenario, ensuring that theload transfers to be feasible at any time. The modeling of powerload profiles was performed from typical load curves measuredin the concession area of AES Sul [15].

III. ALGORITHM FOR CALCULATING RELIABILITY INDEX

The criteria adopted for the remotely controlled switches al-location was the improvement of the reliability indices. For thispurpose, three indicators were chosen from [16]. They corre-spond to the expected values of:

• system average interruption frequency index (per yr)

total number of customer interruptionsTotal number of customers served

(1)

• system average interruption duration index (h/yr)

Interrupted customers Interruption durationTotal number of customers served

(2)• energy not supplied index (kilowatt-hours/yr)

Interrupted Power Interruption Duration.

(3)

These indices can be obtained from the logical-structural ma-trix (LSM) [17], which includes the following input data:

• annual failure rate ;• mean time to restore power supply (TR);• number of customers served by distribution transformers

or primary consumers (N);• load and active power of the distribution transformer or

primary consumers (L).It is highlighted that the time to restore power supply is com-

posed by:• mean time of wait (TW): time interval to respond for the

emergency occurred; it is bounded by the knowledge ofthe existence of an occurrence and the time taken for the


Fig. 1. Distribution network.

authorization of the emergency crew to take care of thecontingency;

• mean time to travel (TTr): time interval between the mo-ment of authorization of the emergency crew until the mo-ment of arrival at the scene;

• mean time to repair or service (TS): time interval betweenthe instant of the emergency crew arriving at the scene untilthe moment the power supply is restored, for each occur-rence of an emergency.

Each column of the matrix corresponds to the branches of thedistribution network protected by a specific protective device orswitching equipment. Each row of the matrix corresponds to thedistribution transformers or to primary consumers. In the cells ofthe logical-structural matrix, there are initial values of the meantime to power restoration. In order to define these values, ananalysis of how long it takes to restore the power supply for thecorresponding consumers (matrix line) is required, when theyare faced with a failure in the distribution network assuming theprotective and switching equipments installed on the network(matrix column).

In the presence of switching equipment, one must evaluatethe possibilities for switching, isolating defects, or transferringloads through these devices. The first possibility is sectional-izing, which corresponds to the isolation of the segment underfailure and other associated nodes downstream of normallyclosed (NC) from nodes upstream. The mean time to isolate(TI) is computed for consumers on all these upstream nodes.The second option is the transfer of the nodes downstreamfrom the NC switch when an upstream fault occurs, then themean time to transfer (TT) is considered for the consumersdownstream. The last possibility depends on the existence ofa normally open (NO) switch downstream from the NC, andthe adjacent feeder must have available technical capacity toreceive the loads that will be transferred. For manual switches,the TI and TT also include mean time of wait (TW) and meantime to travel (TTr). For automatic switches, the TI and TTare much shorter, because there is no TW and TTr. Normally,

.In the case of protective devices, they interrupt the short-

circuit current, not allowing a defect downstream to reach thenodes upstream. Thus, these nodes are not affected by the failureand, therefore, do not have the power supply interrupted.

To illustrate, the logical-structural matrix for the simplifieddistribution network of Fig. 1 will be shown. It is assumed thatthe NO switch at node 5 is connected to another feeder with the

TABLE ILOGICAL-STRUCTURAL MATRIX TO THE DISTRIBUTION NETWORK OF FIG. 1

TABLE IILOGICAL-STRUCTURAL MATRIX WITH TIMES VERSUS FAILURE RATE

technical capability to receive loads downstream from the NCswitch.

Table I shows the construction of the logical-structural matrixfor the example in Fig. 1, considering the mean time to powerrestoration of node i , and constants time to isolation (TI)and time to transfer (TT) for each device.

One can note that for the outage of the circuit breaker (CB-1),the total time to restore power for all consumers is computed,except for those downstream of the NC switch, for which thetransfer time to another feeder is considered. For failures down-stream from the NC switch, the time to isolate the fault for up-stream consumers of the switch and the total time to restorepower to its downstream customers is computed. Regarding theoutage of fuses (FU-1 and FU-2), it only affects its downstreamconsumers, so the total time to restore power is computed. Theupstream nodes are not affected by the fault and do not sufferinterruption, since the fuse is coordinated to blow before the CBtrips (trip-saving scheme).

Then, the matrix values are multiplied by the failure rate ofthe respective equipment , as shown in Table II.

The reliability index is then calculated from the LSM. To cal-culate the expected value of SAIDI, the terms of each row ofTable II are added and then multiplied by the respective amountof consumers in that row, and then the results of all lines areadded together and divided by the total number of customersserved, as follows:

(4)

where ESAIDI is the expected value of the system average in-terruption duration index (h/yr); is the element in row i and


column j of LSM; is the number of consumers for the row i;is the total number of customers served; is the number of

rows; and is the number of columns.The expected value of ENS is straightforward obtained by

replacing the number of consumers in (4) by its respective load,active power of the distribution transformers, and ignoring thetotal number of customers served

(5)

where

EENS expected value of energy not supplied (inkilowatt-hours/yr);

element in row i and column j of LSM;

average load, maximum demand of active powermultiplied by the respective load factor, associatedwith row i (in kilowatts);

n number of rows;

m number of columns.

To obtain the expected value of SAIFI, the process is similarto the SAIDI, requiring only replacement of the logical-struc-tural matrix average times (TR, TI, and TT) by 1, and so, onlyconsider the failure rates

(6)

where

ESAIFI expected value of the system average interruptionfrequency (failures/yr);

element in row i and column j of LSM, withoutconsidering the mean times;

number of customers for row i;

total number of customers served;

n number of rows;

m number of columns.

IV. PROPOSED METHODOLOGY FOR THE ALLOCATION OF

REMOTELY CONTROLLED SWITCHES

When observing the current trends in the automation of dis-tribution networks, the use of remotely controlled switches ispretty convenient. Considering the system’s gradual updating,it becomes necessary to define the priority points for installingthis equipment in order to obtain the highest return rates for theelectric utility.

Therefore, the main purpose of the proposed methodology isto define the best place for allocating a pair of remotely con-trolled switches in distribution networks: an NC switch is to beinstalled in the main trunk feeder and an NO switch in the tieswitch with another feeder. Next, the objective functions and the

constraints should be defined in order to have the whole problemformulated.

In this study, the objective functions were defined to improvethe expected values of the reliability indices SAIDI, SAIFI, andENS. The criteria related to the technical feasibility of loadtransfer through the remotely controlled switches were assumedas constraints.

That is, these transfers may not result in overloading the elec-trical elements, violating the permissible limits of the protectivedevices, and do not violate the permissible voltage range limitsof the primary networks. The following equations reflect all ofthese concepts and complete the proposed formulation to theswitch allocation problem considered:

Objective functions• Minimization of the expected value of SAIDI

(7)

• Minimization of the expected value of SAIFI

(8)

• Minimization of the expected value of ENS

(9)

Constraints:• radial network;• current magnitude of each element must lie within their

permissible limits

(10)

• current magnitude of each protection device must lie withinits permissible limits

(11)

• voltage magnitude of each node must lie within its permis-sible ranges

(12)

where

ESAIDI function;

ESAIFI function;

EENS function;

current at branch i;

maximum current accepted at branch i;

current limit threshold of the protection device j;

voltage magnitude at node j;

minimum voltage magnitude accepted at node j;

maximum voltage magnitude accepted at node j.


TABLE IIILOGICAL-STRUCTURAL MATRIX CONSIDERING THE IMPACT OF

REMOTE-CONTROLLED SWITCHES

The load-flow algorithm checks whether the constraints arenot violated for those points related to the installation of re-motely controlled switches, considering the maximum load pro-file by properly representing the most severe operation scenario.In order to obtain the main functions, it is necessary to adjust theLSM to consider the impact of remotely controlled switches, asshown in the next section.

A. Proposed Methodology to Consider Remotely ControlledSwitches in the Reliability Index

Defining the most convenient locations for installing re-motely controlled switches (NC in the main trunk feeder andNO in the tie switch) in distribution networks involves thecalculation of the reliability index several times, once for eachcouple of candidate points, in order to verify the values ofreduction of the objective functions (ESAIDI, ESAIFI, andEENS) compared to the original configuration.

Thus, the proposed approach for calculating the reliabilityindex considering the impact of remotely controlled switchesbecomes straightforward, since it only changes the cells ofthe logical-structural matrix (LSM) affected by these switcheswithout reconstructing the entire matrix.

For demonstration purposes, consider that the NC and NOswitches of the distribution network of Fig. 1 are remotely con-trolled. With this assumption, fault isolation and load trans-fers can be safely and quickly performed by remote operation,avoiding crew travel time and manual procedures.

In the case of failures downstream of the NC remotely con-trolled switch, the values in the LSM of the nodes upstream ofthe equipment may be changed to zero, since momentary in-terruptions are not computed and the defect is isolated in lessthan 3 min due to remote operation. For those defects upstream,the mean time of remote transferring (TRT) of the loads is con-sidered, typically around 5 min, attributing this value to nodesdownstream from the switch. Table III shows the result of thisprocedure applied to Table II, with the NC and NO switches ofthe example of Fig. 1 remotely controlled.

It can be noted that the columns related to failures down-stream from the fuses have not changed since the remotely con-trolled switches do not have any influence on these failures.

As mentioned before, with the proposed approach, onlythe LSM cells affected by remotely controlled switches are

changed, reducing the processing time and making it possibleto address actual distribution systems. This greatly simplifiesthe calculation of the reliability indices, especially when it isconsidered to be a real distribution system, in which severalalternatives are tested.

B. Selection of Points Applied for the Allocation of RemotelyControlled Switches

The first procedure that takes place when considering the in-stallation of remotely controlled switches is the definition of thesubset of points which are able to receive these devices. Thecriterion proposed in this paper is based on analysis of the tieswitches between feeders, by identifying for each NO remotelycontrolled switch which are the points of the network trunk thatcan receive the remotely controlled NC switch without causinga violation of the constraints.

All of the load transfers are analyzed by heuristic search proce-dures based on the branch-exchange strategy [18], always main-taining a radial configuration and respecting all constraints in anattempt to produce new and promising configurations when ob-serving the objective functions defined. These procedures corre-spond to opening one switch and closing another one.

In order to reduce the number of alternatives, the analysis be-gins with allocation of an NC remotely controlled switch at thepoint of the main trunk feeder farthest from the NO remotelycontrolled switch (tie switch), repeating this procedure for all lo-cations on this path toward the tie switch considered. Moreover,each analysis takes into account all previous constraints . When-ever a feasible point is identified, this process is interrupted andall points downstream from this one toward the tie switch arealso assumed to be feasible, since the load to be transferred issmaller or equal to the load determined as feasible. After that,the original configuration is restored and the analysis goes onwith another tie switch in order to identify all points to receiveremotely controlled switches without violating any of the defineconstraints.

From this identification, the expected values of improvementof indices SAIDI, SAIFI, and ENS (objective functions) are de-termined for each pair of candidate points. It must be empha-sized that this process is extremely fast because it only changesthe LSM cells affected by remotely controlled switches withoutrequiring the recalculation of the entire matrix again.

C. AHP Method

This section presents the proposed algorithm for determiningthe allocation of the remotely controlled switches, based on mul-ticriteria analysis. The main challenge is to define which arethe best places for allocating a couple of remotely controlledswitches when three objective functions are considered. For ex-ample, a particular option may have the greatest reduction ofESAIDI, another with ESAIFI, and another with EENS. A de-cision-making algorithm is the key for deciding which optionshould be chosen.

The AHP method is used as the decision-making techniquefor our approach because of its efficiency in handling quantita-tive and qualitative criteria for the problem resolution. The first


TABLE IVINTENSITY SCALE OF IMPORTANCE [20]

step of AHP is to clearly state the goal and recognize the al-ternatives that could lead to it. Since there are often many cri-teria considered important in making a decision, the next stepin AHP is to develop a hierarchy of the criteria with the moregeneral criteria at the top of the hierarchy. Each top-level cri-teria is then examined to check whether it can be decomposedinto subcriteria. The next step in the AHP is to determine therelative importance of each criterion against all other criteriait is associated with (i.e., establish weights for each criterion).The final step in the AHP is for each alternative to be comparedagainst all other alternatives on each criterion on the bottom ofthe hierarchy of the criteria. The result will be a hierarchy ofthe alternatives complying with the staged goal according to thedefined hierarchy of the criteria and their weights [19].

In the proposed approach, the main criterion is to improve thereliability indices, and the subcriteria are to reduce the value ofESAIDI, ESAIFI, and EENS. The alternatives are the selectedpairs for the allocation of remotely controlled switches.

The steps of the AHP algorithm are [12] as follows.1) Set up the hierarchy model.2) Construct a judgment matrix. The value of elements in

the judgment matrix reflects the user’s knowledge aboutthe relative importance between every pair of factors. Asshown in Table IV, the AHP creates an intensity scale ofimportance to transform these linguistic terms into numer-ical intensity values.Assuming to be the set of objective func-tions, the quantified judgments on pairs of objectives arethen represented by an -by- matrix

...

(13)

where is the number of objective functions, and the en-tries are defined by the followingrules:• if , then , where is an intensity value

determined by the operators, as shown in Table IV;• if is judged to be of equal relative importance as ,

then , and ; in particular, forall i.

3) Calculate the maximal eigenvalue and the correspondingeigenvector of the judgment matrix M. The weighting

vector containing weight values for all objectives is thendetermined by normalizing this eigenvector. The form ofthe weighting vector is as follows:

...(14)

4) Perform a hierarchy ranking and consistency checking ofthe results. To check the effectiveness of the correspondingjudgment matrix in (13), an index of consistency ratio (CR)is calculated as follows [21]:

(15)

where is the largest eigenvalue of matrix M, and RIis the random index.

A table with the order of the matrix and the RI value can befound in [12]. In general, a consistency ratio of 0.10 or less isconsidered acceptable.

The AHP method was implemented in the proposed method-ology and the following results were obtained:

(16)

where

M judgment matrix;

ESAIDI;

ESAIFI;

ENS.

Thus, the weight values for the three objective functions weredetermined

(17)

where is 3.07.The consistency ratio (CR) was calculated by (15)

(18)

The consistency ratio is lower than 0.10 and it is consideredacceptable.

Fig. 2 illustrates an example where five switches constitutingfive pairs of candidate points for receiving the remotely con-trolled switches were considered.

Tables V and VI show the application of the AHP methodfor selecting the best options to allocate pairs of remotely con-trolled switches, considering that there is no violation on theconstraints. According to the proposed method, option “5” isconsidered as the best solution, followed by options “3,” “1,”“4,” and “2,” respectively.


Fig. 2. Example of a distribution network.

TABLE VRESULTS OF THE ANALYSIS FOR EACH ALLOCATION

TABLE VINORMALIZED VALUES OF TABLE V

The results for switches allocation using the AHP methodwere obtained by

(19)

V. EXPERIMENTAL ANALYSIS

The proposed methodology has been applied to case studiesof the AES Sul power utility in Brazil in order to verify itssuitability. The network considered involves the metropolitanarea of AES Sul, with 10 feeders, 1050 pieces of protective

Fig. 3. Result of the analysis for allocation of the remotely controlled switches.

TABLE VIIRESULTS OBTAINED WITH THE USE OF REMOTELY CONTROLLED SWITCHES

and switching equipment, 60 tie-switches, and 3000 distribu-tion transformers.

The algorithm starts searching for candidate points of feedersthat can receive the NC remotely controlled switch throughthe analysis of the technical feasibility of the load transfer toother feeders, as detailed in the previous section. If the loadsdownstream from the analyzed point can be transferred toother feeders without violating the constraints, the point underconsideration can receive the remotely controlled switch.

The results obtained for the couple of switches tested are thenverified, always considering the gains when comparing the ini-tial configuration and with regard to the reduction of reliabilityindices (ESAIDI, ESAIFI, and EENS). Finally, the multicriteriadecision-making method is applied based on the AHP algorithmfor calculating an overall indicator for each couple of switches.Therefore, the couple of switches that present the highest indi-cator value are considered as the best solution for the allocationof the remotely controlled switches. Fig. 3 shows a screen of thedeveloped software with the ranking of points chosen for allo-cation of the remotely controlled switches, and Table VII showsthe results obtained by the application of this methodology incase of feeder outage when considering the power restorationtime.

AES Sul has installed in its distribution network a pair ofremotely controlled switches—NC and NO—allocating them in


the places that would present the best results as indicated by thedeveloped tool. It follows the operation strategy of the switcheswhen there is a feeder outage:

• Fault downstream from NC remotely controlled switch.In the event of fault, the current values of the short cir-cuit will be flagged online in the supervisory control anddata acquisition (SCADA) system. So it is assumed that thefailure occurred downstream from the NC remotely con-trolled switch; then, the NC remotely controlled switch op-erates automatically to isolate the defect. Momentary out-ages are not included in the accounting of the SAIFI inthe Brazilian regulatory policy, and the time to open theswitch since the fault is detected is less than 3 min. It hasbeen taken into consideration that the upstream consumersdo not suffer a permanent failure, and their mean time torestore energy is zero, despite them really suffering a mo-mentary interruption. For the downstream customers, thetotal time to restore energy is computed.

• Fault upstream from the NC remotely controlled switch.In the event of fault, the current values of the short cir-cuit will not be flagged in the SCADA system. So it isassumed that the failure that occurred upstream from theNC remotely controlled switch, automatically operates theremotely controlled switches to open the NC switch andto close the NO one in order to transfer consumers down-stream from the NC switch to another feeder. Since thetransfer time is, on average, 5 min, this time is consideredfor the consumers transferred (i.e., downstream from theswitch). For the consumers upstream, the total time to re-store energy is computed.

Finally, it should be noted that a reduction of approximately30% on the annual SAIDI index of this feeder is expected, as-suming the number of faults in the main trunk feeder.

VI. CONCLUSION

The main contributions of this paper are the methodology toconsider the impact of remotely controlled switches when cal-culating the reliability index (ESAIDI, ESAIFI, and EENS) andthe AHP algorithm for multicriteria decision making for allo-cating the switches.

In addition, the flexibility of the proposed methodology pro-vides a wider comprehension for the computer system devel-oped, resulting in a useful, reliable, and easy-to-use tool for util-ities. For a better evaluation of the software’s performance, casestudies were carried out with actual systems and the results haveproven its suitability.

ACKNOWLEDGMENT

The authors would like to thank AES Sul DistribuidoraGaúcha de Energia SA, Conselho Nacional de Desenvolvi-mento Científico e Tecnológico (CNPq), Fundação de Amparoà Pesquisa do Estado do Rio Grande do Sul (FAPERGS), andCoordenação de Aperfeiçoamento de Pessoal de Nível Superior(CAPES) for their technical support.

REFERENCES

[1] H. P. Schmidt, N. Ida, N. Kagan, and J. C. Guaraldo, “Fast reconfig-uration of distribution systems considering loss minimization,” IEEETrans. Power Syst., vol. 20, no. 3, pp. 1311–1319, Aug. 2005.

[2] R. E. Brown, “Impact of smart grid on distribution system design,” inProc. IEEE Power Energy Soc. Gen. Meeting, 2008, pp. 1–4.

[3] M. Sperandio, E. A. C. Aranha Neto, E. T. Sica, F. Trevisan, C. C.B. Camargo, J. Coelho, and R. Ramos, “Automation planning of loopcontrolled distribution feeders,” presented at the 2nd Int. Elect. Eng.Conf., Coimbra, Portugal, 2007.

[4] G. Levitin, S. Mazal-Tov, and D. Elmakis, “Optimal sectionalizer al-location in electric distribution systems by genetic algorithm,” Elect.Power Syst. Res., vol. 31, pp. 97–102, 1994.

[5] R. Billinton and S. Jonnavithula, “Optimal switching device placementin radial distribution systems,” IEEE Trans. Power Syst., vol. 11, no. 3,pp. 1646–1651, Jul. 1996.

[6] G. Celli and F. Pilo, “Optimal sectionalizing switches allocation indistribution networks,” IEEE Trans. Power Syst., vol. 14, no. 3, pp.1167–1172, Jul. 1999.

[7] J.-H. Teng and Y.-H. Liu, “A novel ACS-based optimum switch relo-cation method,” IEEE Trans. Power Syst., vol. 18, no. 1, pp. 113–120,Feb. 2003.

[8] L. G. W. Silva, R. A. F. Pereira, J. R. Abbade, and J. R. S. Mantovani,“Optimised placement of control and protective devices in electric dis-tribution systems through reactive tabu search algorithm,” Elect. PowerSyst. Res., vol. 78, pp. 372–381, 2008.

[9] P. W. Cox, “Self-healing networks: Performance improvement by au-tomated switching algorithm,” in Proc. CIRED 20th Int. Conf. Elect.Distrib., Jun. 2009, pp. 1–8.

[10] T. Wagner, “Impact of remote controlled switches on distribution gridrecovering processes,” M.Sc. dissertation, School Elect. Eng., RoyalInst. Technol., Stockholm, Sweden, 2010.

[11] F. T. Asr and A. Kazemi, “Determining optimum location of automatedswitches in distribution network,” in Proc. Elect. Power Conf., Canada,2008, pp. 1–6.

[12] T. L. Saaty, The Analytic Hierarchy Process: Planning, Priority Set-ting, Resource Allocation. New York: McGraw-Hill, 1980.

[13] T. L. Saaty, “Highlights and critical points in the theory and applica-tion of the analytic hierarchy process,” Eur. J. Oper. Res., vol. 52, pp.426–447, 1994.

[14] W. H. Kersting and D. L. Mendive, “An application of ladder networktheory to the solution of three-phase radial load-flow problems,” inProc. IEEE Power Eng. Soc. Winter Meeting, 1976, vol. A76 044-8,pp. 1–6.

[15] D. P. Bernardon, L. Comassetto, and L. N. Canha, “Studies of paral-lelism in distribution networks served by different-source substations,”Elect. Power Syst. Res., vol. 78, pp. 450–457, 2008.

[16] R. E. Brown, Electric Power Distribution Reliability, 2nd ed. BocaRaton, FL: CRC, 2009.

[17] V. A. Popov, L. N. Canha, F. A. Farret, A. R. Abaide, D. P. Bernardon,A. L. Konig, L. Comassetto, and A. Pilon, “Algorithm of reliabilityoptimization for operational planning of distribution systems,” in Proc.IEEE/Power Eng. Soc. Transm. Distrib. Conf. Expo. Latin America,2004, pp. 523–528.

[18] R. Cherkaoui, A. Bart, and A. J. Germond, “Optimal configuration ofelectrical distribution networks using heuristic methods,” in Proc. 11thPower Systems Computation Conf., Zurich, Switzerland, 1993, vol. 1,pp. 147–154.

[19] T. Baricevic, E. Mihalek, A. Tunjic, and K. Ugarkovic, “AHP methodin prioritizing investments in transition of MV network to 20 kV,” inProc. CIRED 20th Int. Conf. Elect. Distrib., Jun. 2009, pp. 1–4.

[20] H. T. Yang and S. L. Chen, “Incorporating a multi-criteria decision pro-cedure into the combined dynamic programming/production simula-tion algorithm for generation expansion planning,” IEEE Trans. PowerSyst., vol. 4, no. 1, pp. 165–175, Feb. 1989.

[21] T. L. Saaty and L. T. Tran, “On the invalidity of fuzzifying numericaljudgments in the analytic hierarchy process,” Math.Comput. Model.,vol. 46, pp. 962–975, 2007.

Daniel Pinheiro Bernardon was born in Santa Maria, Brazil, in 1977. He re-ceived the Dr.Eng. degree from Federal University of Santa Maria, Santa Maria,in 2007.

Currently, he is a Professor of Electrical Engineering at Federal University ofPampa, where he has been since 2008. His research interests include distributionsystem analysis, planning, and operation.


Mauricio Sperandio was born in Santa Maria, Brazil, in 1979. He received theDr.Eng. degree from Federal University of Santa Catarina in 2008.

Currently, he is a Professor of Electrical Engineering at Federal Universityof Pampa, Algrete, where he has been since 2009. His research interests includepower systems analysis, planning, and operation.

Vinícius Jacques Garcia was born in Santo Ângelo, Brazil, in 1976. He re-ceived the Dr.Eng. degree from State University of Campinas, Campinas, Brazil,in 2005.

Currently, he is a Professor of Computer and Electrical Engineering at Fed-eral University of Pampa, Algrete, where he has been since 2006. His researchinterests include heuristics and combinatorial optimization.

Luciane Neves Canha was born in Santa Maria, Brazil, in 1971. She receivedthe Dr.Eng. degree from Federal University of Santa Maria in 2004.

Currently, she is a Professor of Electrical Engineering at Federal Universityof Santa Maria, where she has been since 1997. Her research interests includepower systems analysis, planning, and distributed generation.

Alzenira da Rosa Abaide was born in Santa Maria, Brazil. She received theDr. Eng. degree from Federal University of Santa Maria, Santa Maria, in 2005.

She was an Engineer from 1986 to 1988 in the State Company of ElectricEnergy. She has been a Professor and Researcher at Federal University of SantaMaria since 1989. Her research interests include multicriteria analysis appliedto distribution systems.

Eric Fernando Boeck Daza was born in 1983 in Santa Maria, Brazil. He re-ceived the M.Eng. degree from Federal University of Santa Maria, Santa Maria,in 2010.

He has been an Engineer with AES Sul Power Utility since 2007. His researchinterests include distribution system analysis and operation.

AHP Decision-Making Algorithm to Allocate Remotely Controlled Switches in Distribution Networks

Documents

Transcript of AHP Decision-Making Algorithm to Allocate Remotely Controlled Switches in Distribution Networks