AHP Decision-Making Algorithm to Allocate Remotely ... - Unipampa

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AHP Decision-Making Algorithm to Allocate Remotely Controlled Switches in Distribution Networks Daniel 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 permanent concern from the utilities, pursued with the development of technological solutions in order to improve the performance of network restoration conditions. Using remotely controlled switches corresponds to one possible approach to reach such an improvement and gives some convenient remote resources, such as the fault detect, isolation, and transfer loads. This paper presents a methodology implemented in a computer programming language for allocation of these devices in electric distribution systems based on the analytic hierarchical process (AHP) method. The main contributions focus on considering the impact of installing remotely controlled switches in the reliability indices and the AHP decisionmaking algorithm for the switches allocation. The effectiveness of the proposed algorithm is demonstrated with case studies involving actual systems of the AES Sul utility located in Southern Brazil. Index Terms—Analytic hierarchical process (AHP) decision making, distribution networks, logical-structural matrix, reliability, remote-controlled switches.

I. INTRODUCTION

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TILITIES have concentrated on significant efforts to improve the continuity of the supplied electrical energy, especially due to regulatory policies, besides customer satisfaction and improving the amount of energy available to commercial and industrial activities. Moreover, supply interruptions are inevitable due to the implementation of the expansion of the system, preventive maintenance on network components, or even by the action of protective devices due to defects [1].

Manuscript received October 01, 2010; revised January 12, 2011; accepted February 20, 2011 This work was supported in part by AES Sul Distribuidora Gaúcha de Energia SA, in part by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), in part by Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS), and in part by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). Paper no. TPWRD-007552010. D. P. Bernardon, M. Sperandio, and V. J. Garcia are with the Federal University of Pampa, Algrete 97546-550, Brazil (e-mail: daniel.bernardon@unipampa. edu.br; [email protected]; [email protected]. br). 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]. br). 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 online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TPWRD.2011.2119498

When facing a contingency situation, to act as soon as possible may result in a minimally affected area. Whenever a fault is identified at any point of the network, the following procedures must be performed: identifying the right defect position; isolating the part of the distribution system as much as possible by opening normally closed switches; restoring the power supply to consumers downstream of the isolated block; correcting the problem; and reoperating the switches to get back to the normal network status. Currently, a topic that is being frequently discussed is how electric power distribution systems will be in the future. In this sense, the term “Smart Grid” was defined to describe how this new network should behave, that is in a “smart” or “intelligent” way. Among the features of a smart grid are the ability to carry out maneuvers in an automated manner (self-reconfiguration) and high reliability, all with low operation and maintenance costs. A survey of the main projects and research related to smart grid is presented in [2]. Automation of distribution systems plays an important role on reducing the time to implement a service restoration plan with the installation of remote-controlled switches, mainly by allowing the consideration of regulation policies. These devices have shown to be economically viable due to the increase of a large number of suppliers of automation equipment and new communication technologies [3]. The use of an effective methodology for allocating remotecontrolled switches is really important for the utilities, since that procedure is closely related to the restoration time and consequently associated with the reliability index. This kind of solution is not easy to deal with due to its multicriteria, combinatorial nature, and the difficult mathematical modeling. Most studies that are directed to solve the problem of switch allocation in distribution systems [4]–[8] do not deal with remote-controlled switches, which modifies the objective function significantly. The research of Cox [9] and Wagner [10] discusses this subject, but it is limited to strategies for the operation of remote-controlled switches, without covering the allocation. The research by Asr and Kazemi [11] considers the switches allocation; however, a monocriteria approach is used, and the results are limited to small systems. This paper deals with developing computer algorithms to address the remote-controlled switch allocation problem with multiple objectives using an analytic hierarchical process (AHP) [12] in order to improve the reliability index of the distribution systems. The AHP method has proven to be effective in solving

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multicriteria problems, involving many kinds of concerns, including planning, setting priorities, selecting the best among a number of alternatives, and allocating resources. It was developed to assist in making decisions where competing and/or conflicting evaluation criteria exist [13]. Thus, the proposed algorithm can be configured according to the needs of the utilities, helping in the decision-making process. The system will indicate where the resources invested by the utility will bring better operative results concerning the improvement of the reliability index in distribution systems, being characterized as a decision support tool for planning and operating the distribution networks. The proposed approach has proven its effectiveness on a specific portion of the AES Sul distribution system in the sense of improvement in the reliability index and in the reduction of crew displacements, letting one conclude the relevant economic and customer satisfaction benefits obtained. Therefore, the main contributions of this paper are highlighted as follows. • A new method to calculate the impact on the reliability index due to the installation of remote-controlled switches. • A multicriteria decision-making process for solving the remote-controlled switch allocation problem using the AHP method. In addition, the algorithms used for the calculation of the load flow and the reliability indices are also presented, since they are essential for modelling the problem considering the analysis of load transfers. II. ALGORITHM OF LOAD FLOW A version of the classical backward/forward sweep method algorithm was performed to calculate the load flow in radial distribution networks developed by Kersting and Mendive [14]. Since the electrical loads are defined by a constant behavior because of the voltage applied, this results in an unusual solution for calculating the load flow, since the current absorbed by the loads depends on the voltage, and this value is unknown. This way, the solution is found only iteratively. The resulting procedure is described as follows. 1st Stage: It is considered that the voltage in all points of the feeder is the same as the voltage measured in the substation bar. This information can be automatically received by the remote measurement systems installed at the substations. Do not consider voltage drops in the branches at this stage. 2nd Stage: Active and reactive components of the primary currents absorbed and/or injected in the system by the electrical elements are calculated. 3rd Stage: The procedure to obtain the current in all network branches consists of two steps: 1) a search in the node set is performed by adding the current values in the set of branches and 2) currents from the final sections up to the substation are accumulated. 4th Stage: Voltage drops in primary conductors are determined. 5th Stage: From the substation bar, it is possible to obtain the voltage drops accumulated at any other part of the primary network, and, consequently, the voltage values at any point.

6th Stage: The difference between the new voltage values for all nodes and the previous values is checked. If this difference is small enough, the solution for the load-flow calculation was found and the system is said to be convergent. Otherwise, the previous steps are repeated, from step 2 and on, by using the calculated voltages to obtain the current values. Iterations are performed until the found difference is lower than a threshold. In this paper, a threshold of 1% was chosen, because it promotes accurate values for the status variables without requiring too much time to process. At the end of the process, the active and reactive powers and the technical losses in the primary conductors are defined for all feeder branches. This load-flow method was implemented in the proposed methodology for analyzing the technical feasibility of the load transfers, that will influence in the points applied to obtain the allocations of the remote-controlled switches. The criteria related to the technical feasibility of load transfer through the remotely controlled switches were considered constraints. That is, these transfers may not result in overloading the electrical elements, violating the permissible limits of the protective devices and do not violate the permissible voltage range limits of the primary networks. The checking of the constraints is performed considering the maximum load profile by representing properly the most severe operation scenario, ensuring that the load transfers to be feasible at any time. The modeling of power load profiles was performed from typical load curves measured in the concession area of AES Sul [15]. III. ALGORITHM FOR CALCULATING RELIABILITY INDEX The criteria adopted for the remotely controlled switches allocation was the improvement of the reliability indices. For this purpose, three indicators were chosen from [16]. They correspond to the expected values of: • system average interruption frequency index (per yr) total number of customer interruptions Total number of customers served (1) • system average interruption duration index (h/yr)

Interrupted customers Interruption duration Total 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 matrix (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).

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It is highlighted that the time to restore power supply is composed by: • mean time of wait (TW): time interval to respond for the emergency occurred; it is bounded by the knowledge of the existence of an occurrence and the time taken for the authorization of the emergency crew to take care of the contingency; • mean time to travel (TTr): time interval between the moment of authorization of the emergency crew until the moment of arrival at the scene; • mean time to repair or service (TS): time interval between the instant of the emergency crew arriving at the scene until the moment the power supply is restored, for each occurrence of an emergency. Each column of the matrix corresponds to the branches of the distribution network protected by a specific protective device or switching equipment. Each row of the matrix corresponds to the distribution transformers or to primary consumers. In the cells of the logical-structural matrix, there are initial values of the mean time to power restoration. In order to define these values, an analysis of how long it takes to restore the power supply for the corresponding consumers (matrix line) is required, when they are faced with a failure in the distribution network assuming the protective and switching equipments installed on the network (matrix column). In the presence of switching equipment, one must evaluate the possibilities for switching, isolating defects, or transferring loads through these devices. The first possibility is sectionalizing, which corresponds to the isolation of the segment under failure and other associated nodes downstream of normally closed (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 downstream from the NC switch when an upstream fault occurs, then the mean time to transfer (TT) is considered for the consumers downstream. The last possibility depends on the existence of a normally open (NO) switch downstream from the NC, and the adjacent feeder must have available technical capacity to receive the loads that will be transferred. For manual switches, the TI and TT also include mean time of wait (TW) and mean time to travel (TTr). For automatic switches, the TI and TT are much shorter, because there is no TW and TTr. Normally, . In the case of protective devices, they interrupt the shortcircuit current, not allowing a defect downstream to reach the nodes upstream. Thus, these nodes are not affected by the failure and, therefore, do not have the power supply interrupted. To illustrate, the logical-structural matrix for the simplified distribution network of Fig. 1 will be shown. It is assumed that the NO switch at node 5 is connected to another feeder with the technical capability to receive loads downstream from the NC switch. Table I shows the construction of the logical-structural matrix for the example in Fig. 1, considering the mean time to power , and constants time to isolation (TI) restoration of node i 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,

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Fig. 1. Distribution network.

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

TABLE II LOGICAL-STRUCTURAL MATRIX WITH TIMES VERSUS FAILURE RATE

except for those downstream of the NC switch, for which the transfer time to another feeder is considered. For failures downstream from the NC switch, the time to isolate the fault for upstream consumers of the switch and the total time to restore power to its downstream customers is computed. Regarding the outage of fuses (FU-1 and FU-2), it only affects its downstream consumers, so the total time to restore power is computed. The upstream nodes are not affected by the fault and do not suffer interruption, since the fuse is coordinated to blow before the CB trips (trip-saving scheme). Then, the matrix values are multiplied by the failure rate of , as shown in Table II. the respective equipment The reliability index is then calculated from the LSM. To calculate the expected value of SAIDI, the terms of each row of Table II are added and then multiplied by the respective amount of consumers in that row, and then the results of all lines are

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added together and divided by the total number of customers served, as follows:

(4) where ESAIDI is the expected value of the system average inis the element in row i and terruption duration index (h/yr); is the number of consumers for the row i; column j of LSM; 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 the total number of customers served (5) where EENS expected value of energy not supplied (in kilowatt-hours/yr);

this equipment in order to obtain the highest return rates for the electric utility. Therefore, the main purpose of the proposed methodology is to define the best place for allocating a pair of remotely controlled switches in distribution networks: an NC switch is to be installed in the main trunk feeder and an NO switch in the tie switch with another feeder. Next, the objective functions and the constraints should be defined in order to have the whole problem formulated. In this study, the objective functions were defined to improve the expected values of the reliability indices SAIDI, SAIFI, and ENS. The criteria related to the technical feasibility of load transfer through the remotely controlled switches were assumed as constraints. That is, these transfers may not result in overloading the electrical elements, violating the permissible limits of the protective devices, and do not violate the permissible voltage range limits of the primary networks. The following equations reflect all of these concepts and complete the proposed formulation to the switch allocation problem considered: Objective functions • Minimization of the expected value of SAIDI (7)

element in row i and column j of LSM; average load, maximum demand of active power multiplied by the respective load factor, associated with row i (in kilowatts); n

number of rows;

m

number of columns.

• Minimization of the expected value of SAIFI (8) • Minimization of the expected value of ENS

To obtain the expected value of SAIFI, the process is similar to the SAIDI, requiring only replacement of the logical-structural matrix average times (TR, TI, and TT) by 1, and so, only consider the failure rates

(9)

(6)

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

where ESAIFI expected value of the system average interruption frequency (failures/yr); element in row i and column j of LSM, without considering the mean times;

(10) • current magnitude of each protection device must lie within its permissible limits (11)

number of customers for row i; total number of customers served; n

number of rows;

m

number of columns.

• voltage magnitude of each node must lie within its permissible ranges (12) where

IV. PROPOSED METHODOLOGY FOR THE ALLOCATION OF REMOTELY CONTROLLED SWITCHES When observing the current trends in the automation of distribution networks, the use of remotely controlled switches is pretty convenient. Considering the system’s gradual updating, it becomes necessary to define the priority points for installing

ESAIDI function; ESAIFI function; EENS function; current at branch i;

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maximum current accepted at branch i; current limit threshold of the protection device j;

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TABLE III LOGICAL-STRUCTURAL MATRIX CONSIDERING THE IMPACT OF REMOTE-CONTROLLED SWITCHES

voltage magnitude at node j; minimum voltage magnitude accepted at node j; maximum voltage magnitude accepted at node j. The load-flow algorithm checks whether the constraints are not violated for those points related to the installation of remotely controlled switches, considering the maximum load profile by properly representing the most severe operation scenario. In order to obtain the main functions, it is necessary to adjust the LSM to consider the impact of remotely controlled switches, as shown in the next section. B. Selection of Points Applied for the Allocation of Remotely Controlled Switches A. Proposed Methodology to Consider Remotely Controlled Switches in the Reliability Index Defining the most convenient locations for installing remotely controlled switches (NC in the main trunk feeder and NO in the tie switch) in distribution networks involves the calculation of the reliability index several times, once for each couple of candidate points, in order to verify the values of reduction of the objective functions (ESAIDI, ESAIFI, and EENS) compared to the original configuration. Thus, the proposed approach for calculating the reliability index considering the impact of remotely controlled switches becomes straightforward, since it only changes the cells of the logical-structural matrix (LSM) affected by these switches without reconstructing the entire matrix. For demonstration purposes, consider that the NC and NO switches of the distribution network of Fig. 1 are remotely controlled. With this assumption, fault isolation and load transfers 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 controlled switch, the values in the LSM of the nodes upstream of the equipment may be changed to zero, since momentary interruptions are not computed and the defect is isolated in less than 3 min due to remote operation. For those defects upstream, the mean time of remote transferring (TRT) of the loads is considered, typically around 5 min, attributing this value to nodes downstream from the switch. Table III shows the result of this procedure applied to Table II, with the NC and NO switches of the example of Fig. 1 remotely controlled. It can be noted that the columns related to failures downstream from the fuses have not changed since the remotely controlled switches do not have any influence on these failures. As mentioned before, with the proposed approach, only the LSM cells affected by remotely controlled switches are changed, reducing the processing time and making it possible to address actual distribution systems. This greatly simplifies the calculation of the reliability indices, especially when it is considered to be a real distribution system, in which several alternatives are tested.

The first procedure that takes place when considering the installation of remotely controlled switches is the definition of the subset of points which are able to receive these devices. The criterion proposed in this paper is based on analysis of the tie switches between feeders, by identifying for each NO remotely controlled switch which are the points of the network trunk that can receive the remotely controlled NC switch without causing a violation of the constraints. All of the load transfers are analyzed by heuristic search procedures based on the branch-exchange strategy [18], always maintaining a radial configuration and respecting all constraints in an attempt to produce new and promising configurations when observing the objective functions defined. These procedures correspond to opening one switch and closing another one. In order to reduce the number of alternatives, the analysis begins with allocation of an NC remotely controlled switch at the point of the main trunk feeder farthest from the NO remotely controlled switch (tie switch), repeating this procedure for all locations on this path toward the tie switch considered. Moreover, each analysis takes into account all previous constraints . Whenever a feasible point is identified, this process is interrupted and all points downstream from this one toward the tie switch are also assumed to be feasible, since the load to be transferred is smaller or equal to the load determined as feasible. After that, the original configuration is restored and the analysis goes on with another tie switch in order to identify all points to receive remotely controlled switches without violating any of the define constraints. From this identification, the expected values of improvement of indices SAIDI, SAIFI, and ENS (objective functions) are determined for each pair of candidate points. It must be emphasized that this process is extremely fast because it only changes the LSM cells affected by remotely controlled switches without requiring the recalculation of the entire matrix again. C. AHP Method This section presents the proposed algorithm for determining the allocation of the remotely controlled switches, based on multicriteria analysis. The main challenge is to define which are the best places for allocating a couple of remotely controlled

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switches when three objective functions are considered. For example, a particular option may have the greatest reduction of ESAIDI, another with ESAIFI, and another with EENS. A decision-making algorithm is the key for deciding which option should be chosen. The AHP method is used as the decision-making technique for our approach because of its efficiency in handling quantitative and qualitative criteria for the problem resolution. The first step of AHP is to clearly state the goal and recognize the alternatives that could lead to it. Since there are often many criteria considered important in making a decision, the next step in AHP is to develop a hierarchy of the criteria with the more general criteria at the top of the hierarchy. Each top-level criteria is then examined to check whether it can be decomposed into subcriteria. The next step in the AHP is to determine the relative importance of each criterion against all other criteria it is associated with (i.e., establish weights for each criterion). The final step in the AHP is for each alternative to be compared against all other alternatives on each criterion on the bottom of the hierarchy of the criteria. The result will be a hierarchy of the alternatives complying with the staged goal according to the defined hierarchy of the criteria and their weights [19]. In the proposed approach, the main criterion is to improve the reliability indices, and the subcriteria are to reduce the value of ESAIDI, ESAIFI, and EENS. The alternatives are the selected pairs 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 about the relative importance between every pair of factors. As shown in Table IV, the AHP creates an intensity scale of importance to transform these linguistic terms into numerical intensity values. to be the set of objective funcAssuming tions, the quantified judgments on pairs of objectives are then represented by an -by- matrix

TABLE IV INTENSITY SCALE OF IMPORTANCE [20]

determined by normalizing this eigenvector. The form of the weighting vector is as follows:

.. .

(14)

4) Perform a hierarchy ranking and consistency checking of the results. To check the effectiveness of the corresponding judgment matrix in (13), an index of consistency ratio (CR) is calculated as follows [21]: (15) where is the largest eigenvalue of matrix M, and RI is the random index. A table with the order of the matrix and the RI value can be found in [12]. In general, a consistency ratio of 0.10 or less is considered acceptable. The AHP method was implemented in the proposed methodology and the following results were obtained:

(16)

.. .

(13)

where M

where is the number of objective functions, and the entries are defined by the following rules: , then , where is an intensity value • if determined by the operators, as shown in Table IV; is judged to be of equal relative importance as , • if , and ; in particular, for all then i. 3) Calculate the maximal eigenvalue and the corresponding eigenvector of the judgment matrix M. The weighting vector containing weight values for all objectives is then

judgment matrix; ESAIDI; ESAIFI; ENS.

Thus, the weight values for the three objective functions were determined (17) where

is 3.07.

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The results for switches allocation using the AHP method were obtained by

(19) Fig. 2. Example of a distribution network.

V. EXPERIMENTAL ANALYSIS

TABLE V RESULTS OF THE ANALYSIS FOR EACH ALLOCATION

TABLE VI NORMALIZED VALUES OF TABLE V

The consistency ratio (CR) was calculated by (15)

(18) The consistency ratio is lower than 0.10 and it is considered acceptable. Fig. 2 illustrates an example where five switches constituting five pairs of candidate points for receiving the remotely controlled switches were considered. Tables V and VI show the application of the AHP method for selecting the best options to allocate pairs of remotely controlled switches, considering that there is no violation on the constraints. According to the proposed method, option “5” is considered as the best solution, followed by options “3,” “1,” “4,” and “2,” respectively.

The proposed methodology has been applied to case studies of the AES Sul power utility in Brazil in order to verify its suitability. The network considered involves the metropolitan area of AES Sul, with 10 feeders, 1050 pieces of protective and switching equipment, 60 tie-switches, and 3000 distribution transformers. The algorithm starts searching for candidate points of feeders that can receive the NC remotely controlled switch through the analysis of the technical feasibility of the load transfer to other feeders, as detailed in the previous section. If the loads downstream from the analyzed point can be transferred to other feeders without violating the constraints, the point under consideration can receive the remotely controlled switch. The results obtained for the couple of switches tested are then verified, always considering the gains when comparing the initial configuration and with regard to the reduction of reliability indices (ESAIDI, ESAIFI, and EENS). Finally, the multicriteria decision-making method is applied based on the AHP algorithm for calculating an overall indicator for each couple of switches. Therefore, the couple of switches that present the highest indicator value are considered as the best solution for the allocation of the remotely controlled switches. Fig. 3 shows a screen of the developed software with the ranking of points chosen for allocation of the remotely controlled switches, and Table VII shows the results obtained by the application of this methodology in case of feeder outage when considering the power restoration time. AES Sul has installed in its distribution network a pair of remotely controlled switches—NC and NO—allocating them in the places that would present the best results as indicated by the developed tool. It follows the operation strategy of the switches when there is a feeder outage: • Fault downstream from NC remotely controlled switch. In the event of fault, the current values of the short circuit will be flagged online in the supervisory control and data acquisition (SCADA) system. So it is assumed that the failure occurred downstream from the NC remotely controlled switch; then, the NC remotely controlled switch operates automatically to isolate the defect. Momentary outages are not included in the accounting of the SAIFI in the Brazilian regulatory policy, and the time to open the switch since the fault is detected is less than 3 min. It has

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the AHP algorithm for multicriteria decision making for allocating the switches. In addition, the flexibility of the proposed methodology provides a wider comprehension for the computer system developed, resulting in a useful, reliable, and easy-to-use tool for utilities. For a better evaluation of the software’s performance, case studies were carried out with actual systems and the results have proven its suitability. ACKNOWLEDGMENT

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

TABLE VII RESULTS OBTAINED WITH THE USE OF REMOTELY CONTROLLED SWITCHES

been taken into consideration that the upstream consumers do not suffer a permanent failure, and their mean time to restore energy is zero, despite them really suffering a momentary interruption. For the downstream customers, the total 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 circuit will not be flagged in the SCADA system. So it is assumed that the failure that occurred upstream from the NC remotely controlled switch, automatically operates the remotely controlled switches to open the NC switch and to close the NO one in order to transfer consumers downstream from the NC switch to another feeder. Since the transfer time is, on average, 5 min, this time is considered for the consumers transferred (i.e., downstream from the switch). For the consumers upstream, the total time to restore energy is computed. Finally, it should be noted that a reduction of approximately 30% on the annual SAIDI index of this feeder is expected, assuming the number of faults in the main trunk feeder. VI. CONCLUSION The main contributions of this paper are the methodology to consider the impact of remotely controlled switches when calculating the reliability index (ESAIDI, ESAIFI, and EENS) and

The authors would like to thank AES Sul Distribuidora Gaúcha de Energia SA, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS), and Coordenação 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 reconfiguration of distribution systems considering loss minimization,” IEEE Trans. Power Syst., vol. 20, no. 3, pp. 1311–1319, Aug. 2005. [2] R. E. Brown, “Impact of smart grid on distribution system design,” in Proc. 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 loop controlled distribution feeders,” presented at the 2nd Int. Elect. Eng. Conf., Coimbra, Portugal, 2007. [4] G. Levitin, S. Mazal-Tov, and D. Elmakis, “Optimal sectionalizer allocation 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 placement in 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 in distribution 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 relocation 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 distribution systems through reactive tabu search algorithm,” Elect. Power Syst. Res., vol. 78, pp. 372–381, 2008. [9] P. W. Cox, “Self-healing networks: Performance improvement by automated 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 grid recovering processes,” M.Sc. dissertation, School Elect. Eng., Royal Inst. Technol., Stockholm, Sweden, 2010. [11] F. T. Asr and A. Kazemi, “Determining optimum location of automated switches in distribution network,” in Proc. Elect. Power Conf., Canada, 2008, pp. 1–6. [12] T. L. Saaty, The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. New York: McGraw-Hill, 1980. [13] T. L. Saaty, “Highlights and critical points in the theory and application 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 network theory to the solution of three-phase radial load-flow problems,” in Proc. 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 parallelism 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. Boca Raton, 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 reliability optimization for operational planning of distribution systems,” in Proc. IEEE/Power Eng. Soc. Transm. Distrib. Conf. Expo. Latin America, 2004, pp. 523–528.

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[18] R. Cherkaoui, A. Bart, and A. J. Germond, “Optimal configuration of electrical distribution networks using heuristic methods,” in Proc. 11th Power Systems Computation Conf., Zurich, Switzerland, 1993, vol. 1, pp. 147–154. [19] T. Baricevic, E. Mihalek, A. Tunjic, and K. Ugarkovic, “AHP method in prioritizing investments in transition of MV network to 20 kV,” in Proc. CIRED 20th Int. Conf. Elect. Distrib., Jun. 2009, pp. 1–4. [20] H. T. Yang and S. L. Chen, “Incorporating a multi-criteria decision procedure into the combined dynamic programming/production simulation algorithm for generation expansion planning,” IEEE Trans. Power Syst., vol. 4, no. 1, pp. 165–175, Feb. 1989. [21] T. L. Saaty and L. T. Tran, “On the invalidity of fuzzifying numerical judgments in the analytic hierarchy process,” Math.Comput. Model., vol. 46, pp. 962–975, 2007.

Vinícius Jacques Garcia was born in Santo Ângelo, Brazil, in 1976. He received the Dr.Eng. degree from State University of Campinas, Campinas, Brazil, in 2005. Currently, he is a Professor of Computer and Electrical Engineering at Federal University of Pampa, Algrete, where he has been since 2006. His research interests include heuristics and combinatorial optimization.

Daniel Pinheiro Bernardon was born in Santa Maria, Brazil, in 1977. He received 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 of Pampa, where he has been since 2008. His research interests include distribution system analysis, planning, and operation.

Alzenira da Rosa Abaide was born in Santa Maria, Brazil. She received the Dr. 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 Electric Energy. She has been a Professor and Researcher at Federal University of Santa Maria since 1989. Her research interests include multicriteria analysis applied to distribution systems.

Mauricio Sperandio was born in Santa Maria, Brazil, in 1979. He received the Dr.Eng. degree from Federal University of Santa Catarina in 2008. Currently, he is a Professor of Electrical Engineering at Federal University of Pampa, Algrete, where he has been since 2009. His research interests include power systems analysis, planning, and operation.

Eric Fernando Boeck Daza was born in 1983 in Santa Maria, Brazil. He received 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 research interests include distribution system analysis and operation.

Luciane Neves Canha was born in Santa Maria, Brazil, in 1971. She received the Dr.Eng. degree from Federal University of Santa Maria in 2004. Currently, she is a Professor of Electrical Engineering at Federal University of Santa Maria, where she has been since 1997. Her research interests include power systems analysis, planning, and distributed generation.