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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright Author's personal copy Electric Power Systems Research 81 (2011) 414–420 Contents lists available at ScienceDirect Electric Power Systems Research journal homepage: www.elsevier.com/locate/epsr Methodology for allocation of remotely controlled switches in distribution networks based on a fuzzy multi-criteria decision making algorithm D.P. Bernardon a,∗ , M. Sperandio a , V.J. Garcia a , J. Russi a , L.N. Canha b , A.R. Abaide b , E.F.B. Daza c a UNIPAMPA – Federal University of Pampa, Brazil UFSM - Federal University of Santa Maria, Brazil c AES Sul, Brazil b a r t i c l e i n f o Article history: Received 28 July 2010 Received in revised form 1 October 2010 Accepted 4 October 2010 Available online 29 October 2010 Keywords: Logical-structural matrix Multi-criteria decision making Remotely controlled switches Reliability Distribution networks a b s t r a c t 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 giving 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 these devices in electric distribution systems based on multi-criteria fuzzy analysis. The main contributions are focus on considering the impact of installing remote-controlled switches in the reliability indexes and algorithm of fuzzy multi-criteria decision making 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 the south of Brazil. © 2010 Elsevier B.V. All rights reserved. 1. Introduction Utilities have concentrate significant efforts in order to improve the continuity of electrical energy supplied, specially by understanding this desirable conditions associated with customer satisfaction and with improvement of the amount of energy available to commercial and industrial activities. In addition, such a movement is indispensable when looking forward expansion and frequently maintenance on distribution systems [1]. When facing with a contingency situation, to act as soon as possible may result in a minimum 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 as much as possible the part of the distribution system by opening normally closed switches; restoring the power supply to consumers downstream the isolated block; correcting the problem; reoperating the switches to get back to the normal network status. Currently, a topic that is being frequently discussed is how the 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 ∗ Corresponding author. Tel.: +55 5192811760. E-mail address: [email protected] (D.P. Bernardon). 0378-7796/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.epsr.2010.10.010 high reliability, all with a 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 emergence of a large number of suppliers of automation equipment and to the new communication technologies [3]. The use of an effective methodology to allocate remotecontrolled switches is really important for the utilities, since that procedure is closely related to the restoration time and consequently associated with reliability index. This kind of solution is not easy to deal with due to its multiple criteria. Most studies are limited to develop strategies for operation of remotely controlled switches [4,5], without covering the switches allocation. The research by Asr and Kazemi [6] involves allocation, however a monocriteria approach is used and the results are limited to small systems. This paper deals with the making of computer algorithms to address the remotely controlled switch allocation problem on the basis of a Bellman–Zadeh method [7] with fuzzy multi-criteria analysis, in order to improve the reliability indexes of the distribution systems. This method has proven to be effective in solving multi-criteria problems and promoting final solutions belonging to the Pareto objective space [8]. The algorithm can be configured Author's personal copy D.P. Bernardon et al. / Electric Power Systems Research 81 (2011) 414–420 according to the needs of the utilities, helping in the decision making process. Thus, the proposed system will indicate where the resources invested by the utility will bring better operative results concerning the improving in the reliability indexes of 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 a real distribution system in the sense of the improvement in the reliability index and also in the reduction of crew displacements, letting one concludes the relevant economic and customer satisfaction benefits obtained. Therefore, the main contributions of this paper are highlighted as follow: (a) a new method to calculate the impact on reliability indexes due to the installation of remotely controlled switches; (b) a multi-criteria decision making process for solving the remotecontrolled switch allocation problem using the Bellman–Zadeh method. Additionally, the algorithms used for load flow and for reliability indexes computation are also presented, since they are essential for modeling the problem, considering the analysis of load transfers and the reliability indexes computation. 2. 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 [9]. The solution can only be found iteratively, because the electrical loads are defined by a constant power, so the current absorbed by the loads depends on an unknown value of the voltage. 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 (flat start). 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: (a) a search in the node set is performed adding the current values in the set of branches and (b) 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 on, using the calculated voltages to obtain the current values. Iterations are performed until the difference found 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 procedure, the active and reactive powers, losses, voltages and currents are defined for all the primary feeder’s branches and nodes. 415 This load flow method was implemented in the proposed methodology for analyzing the technical feasibility of the load transfers, which will impact in the set of candidate points to receive the remotely controlled switches. The criteria related to the technical feasibility of load transfer through the remotely controlled switches were considered as constraints. That is, such transfers may not result in overload of any electrical element, violation of the permissible limits of the protective devices or the permissible voltage range limits of the primary networks. The constraints assessment is performed considering the most severe operation scenario, represented by the heaviest load profile, ensuring that the load transfers can be feasible at any time. The modeling of power load profiles was performed from typical load curves measured in the concession area of the AES Sul utility. 3. Algorithm for calculating reliability index The criteria adopted in the remotely controlled switches allocation was the improvement on the reliability index. For this purpose, some indicators were chosen; they correspond to the expected values of [10]: • System Average Interruption Frequency Index: SAIFI = total number of customer interruptions total number of customers served (/year) (1) • System Average Interruption Duration Index: SAIDI = interrupted customers × interruption duration total number of customers served (h/year) (2) • Energy Not Supplied Index: ENS = interrupted power × interruption duration (kWh/year) (3) These indexes can be obtained from the logical-structural matrix (LSM) [11], 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, active power, of the distribution transformer or primary consumers (L). It is highlighted that 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 arrival at the scene until the moment of restoring power supply, 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 Author's personal copy 416 D.P. Bernardon et al. / Electric Power Systems Research 81 (2011) 414–420 Table 2 Logical-structural matrix with times vs. failure rate. Nodes (distribution transformers or primary customers) Fig. 1. Distribution network. 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, it is required to analyze how long it takes to power supply restoration for the corresponding consumers (matrix line), 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 a NC (Normally Closed) from nodes upstream. For all these nodes upstream, the mean time to isolate (TI) for consumers upstream is computed. The second option is the transfer of the nodes downstream from the NC switch when upstream faults occur, being considered as mean time to transfer (TT) for consumers downstream. This transfer depends on the existence of a NO (Normally Open) switch downstream from the NC, and the feeder that will receive the consumers must have the technical capacity available to receive the loads to 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 are not TW and TTr. Normally TR > TT > TI. In the case of protective devices, they interrupt the short circuit current, not allowing that a defect downstream reaches 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 of the NC switch. Table 1 shows the construction of logical-structural matrix for the example in Fig. 1, considering the mean time to 1 2 3 4 5 6 7 8 n m 1 2 3 4 5 6 7 8 i=1 ESAIDI = Switch, NC Fuse, FU-1 Fuse, FU-2 TR1 TR1 TT TT TT TT TT TR1 TI TI TR2 TR2 TR2 TR2 TR2 TI 0 0 0 0 0 TR3 TR3 0 0 0 0 0 0 0 0 TR4 Fuse, FU-1 Fuse, FU-2 TR1 1 TR1 1 TT 1 TT 1 TT 1 TT 1 TT 1 TR1 1 TI 2 TI 2 TR2 2 TR2 2 TR2 2 TR2 2 TR2 2 TI 2 0 0 0 0 0 TR3 3 TR3 3 0 0 0 0 0 0 0 0 TR4 4 j=1 Mi,j · Ni (4) NC n ⎛ ⎝ m i=1 Circuit breaker, CB-1 Switch, NC where ESAIDI = expected value of system average interruption duration index (h/year); Mi,j = element in row i and column j of LSM; Ni = number of consumers for the row i; NC = total number of customers served; n = number of rows; m = number of columns. The expected value of ENS is straightforward obtained by replacing the number of consumers in Eq. (4) by its respective load, active power of the distribution transformers, ignoring the total number of customers served. EENS = Protective and switching equipments Circuit breaker, CB-1 power restoration (TR), isolation (TI) and 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 to those downstream of the NC switch, for which is considered the transfer time to another feeder. For failures downstream of 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, not suffering interruption, since the fuse is coordinated to blow before the circuit breaker trips (trip saving scheme). Then, the values of the matrix are multiplied by the failure rate of the respective equipment, as shown in Table 2. The reliability indexes are then calculated from the LSM. To calculate the expected value of SAIDI, the terms of each row of Table 2 are added and then multiplied by the respective amount of consumers in that row, and then the results of all lines are added together and divided by the total number of customers served, as follows: Table 1 Logical-structural matrix to the distribution network of Fig. 1. Nodes (distribution transformers or primary customers) Protective and switching equipments ⎞ Mi,j ⎠ · Li (5) j=1 where EENS = expected value of energy not supplied (kWh/year); Mi,j = element in row i and column j of LSM; Li = load, active power, associated to row i (kW); n = number of rows; m = number of columns. 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, so are considered only the failure rates. n m ESAIFI = i=1 M∗ j=1 i,j · Ni (6) NC where ESAIFI = expected value age interruption frequency of system (failures/year); aver∗ = Mi,j Author's personal copy D.P. Bernardon et al. / Electric Power Systems Research 81 (2011) 414–420 element in row i and column j of LSM, without considering the mean times; Ni = number of customers for the row i; NC = total number of customers served; n = number of rows; m = number of columns. 4. Proposed methodology for allocation of remotely controlled switches When observing the recent trends in automation of distribution networks, the use of remotely controlled switches is pretty convenient. Considering a gradual update of the distribution system, it becomes necessary to prioritize the points for installing these equipments in order to obtain the highest return rates for the utility. Therefore, the main purpose of the proposed methodology is to define a rank of the best places to allocate pairs of remotely controlled switches on the feeders of a distribution network: a NC switch to be installed in the main trunk feeder and a 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, objective functions were defined to improve the reliability indexes 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, such transfers may not result in overload of the electrical elements, violation of the permissible limits of the protective devices or violate the permissible voltage range limits of the primary networks. The following equations describe all these concepts and complete the proposed formulation of the considered switch allocation problem: Objective functions: • Minimization of the expected value of SAIDI: n m i=1 Min fESAIDI = j=1 Mi,j · Ni NC (7) • Minimization of the expected value of SAIFI: n m M∗ j=1 i,j i=1 Min fESAIFI = · Ni NC (8) • Minimization of the expected value of ENS: Min fEENS = n ⎛ ⎝ i=1 m ⎞ Mi,j ⎠ · Li (9) j=1 Constraints: • Radial network; • Current magnitude of each element must lie within their permissible limits: Ii ≤ Ii max (10) • Current magnitude of each protection device must lie within its permissible limits: Ii ≤ Ij prot (11) • Voltage magnitude of each node must lie within their permissible ranges: Vj min ≤ Vj ≤ Vj max (12) where fESAIDI – ESAIDI function; fESAIFI – ESAIFI function; fEENS – EENS function; Ii – current at branch i; Ii max – maximum current accepted at branch i; Ij prot – current limit threshold of the protection device j; Vj – voltage magnitude at node j; Vj min – minimum 417 voltage magnitude accepted at node j; Vj max – maximum voltage magnitude accepted at node j. The load flow algorithm checks if the constraints are not violated for those points related to the installation of remotely controlled switches, considering the maximum load profile by representing properly the most severe operation scenario. In order to obtain the main functions is necessary to adjust the LSM to consider the impact of remotely controlled switches, as shown in the next section. 4.1. Proposed methodology to consider remotely controlled switches in 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 indexes several times, once for each pair 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 remotely 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 must be changed to zero, since the switch prevents the spreading of the defect in less than 3 min due to the remote sectioning of the feeder. For those defects upstream, it is considered the mean time of remote transferring (TRT) of the loads, typically less than 5 min, attributing this value to nodes downstream of the switch. Table 3 shows the result of this procedure applied to Table 2, 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 of 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 possible to assess real distribution systems. This greatly simplifies the reliability indexes computation, especially when numerous alternatives are tested. 4.2. Selection of candidate points for the allocation of remotely controlled switches 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 feeder trunk that can receive the remotely controlled NC switch without causing violation of the constraints. All the load transfers are analyzed by a heuristic search procedure based on the branch-exchange strategy [12], always maintaining a radial configuration and respecting all constraints in an attempt to produce new and promising configurations when observing the defined objective functions. Author's personal copy 418 D.P. Bernardon et al. / Electric Power Systems Research 81 (2011) 414–420 Table 3 Logical-structural matrix considering the impact of remotely controlled switches. Nodes Protective and Switching Equipments (distribution transformers Circuit Breaker Remotely-Controlled Switch Fuse Fuse or primary customers) CB - 1 NC FU - 1 FU - 2 1 TR1 λ1 0 0 0 2 TR1 λ1 0 0 0 3 TRΤ λ1 TR2 λ2 0 0 4 TRΤ λ1 TR2 λ2 0 0 5 TRT λ1 TR2 λ2 0 0 6 TRΤ λ1 TR2 λ2 TR3 λ3 0 7 TRΤ λ1 TR2 λ2 TR3 λ3 0 8 TR1 λ1 0 0 TR4 λ4 Cells of the logical-structural matrix (LSM) changed of value due the remotely controlled switches. In order to reduce the number of alternatives, the analysis begins with allocation of a 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 towards the considered tie-switch. Moreover, each analysis takes into account all the constraints previously. Whenever a feasible point is identified, this procedure is interrupted and all points downstream of this one towards to the tie-switch are also assumed as 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 defined constraints. The expected values of improvement of ESAIDI, ESAIFI and EENS (objective functions) are determined for each pair of candidate points from this subset. 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 calculation of the entire matrix again. 4.3. The multi-criteria decision making algorithm This section presents the algorithm for determining the allocation of the remotely controlled switches, based on multi-criteria analysis. The main challenge is to define the best places to allocate different pairs of remotely controlled switches when three objective functions are considered, because there is no absolute solution, since there are tradeoffs between these functions. For example, a particular option can have the greatest reduction of ESAIDI, but other can greatly reduce the ESAIFI and another may be the best reduction of EENS. A decision making algorithm is the key for support which option should be chosen. The Bellman–Zadeh method [6] is used as the decision making technique for our approach because of its efficiency in handling quantitative and qualitative criteria for the problem resolution [13]. The objective functions are replaced by other functions in the form of fuzzy sets, with the corresponding membership functions given by Eqs. (13) and (14): one for objective functions to be maximized and another one for objective functions to be minimized, respec- tively: Aj (x) = Fj (x) maxFj (x) , (13) , (14) x ∈ Dx Aj (x) = min Fj (x) x ∈ Dx Fj (x) The maximum intersection between membership functions is the most attractive configuration – i.e. the best solution that balances the multiple criteria, as shown in Eq. (15). maxD (x) = max min Aj (x). x ∈ Dx x ∈ Dx j=1,...,n (15) Fig. 2 illustrates this approach. Tables 4 and 5 illustrate the application of the Bellman–Zadeh algorithm for selecting the best options to allocate pairs of remotely controlled switches, considering there is no violation on the constraints. In this example, five pairs of candidate points for receiving the remotely controlled switches were considered (Fig. 3). The values related to the membership functions of fuzzy solutions, for each option, are obtained with the application of Eq. (13) to maximize the reduction of the reliability indexes, as shown in Table 5: According to the proposed method, it is considered as the best solution the option that has the maximum value of the intersection of the membership functions, given by Eq. (15). In this case, Fig. 2. Intersection between the membership functions. Author's personal copy D.P. Bernardon et al. / Electric Power Systems Research 81 (2011) 414–420 419 Table 4 Results of the analysis for each allocation. Options Couple of analyzed switches ESAIDI reduction (hours-year) ESAIFI reduction (failures/year) EENS reduction (kWh/year) 1 2 3 4 5 – NC-1 and NO-1 NC-1 and NO-2 NC-1 and NO-3 NC-2 and NO-1 NC-2 and NO-2 Reference value 1.8 0.3 1.6 0.4 1.4 1.8 0.2 0.1 0.4 0.2 0.8 0.8 130.70 178.42 161.39 106.04 113.17 178.42 Table 5 Membership functions values and fuzzy solutions. Options ESAIDI reduction (hours-year) ESAIFI reduction (failures/year) EENS reduction (kWh/year) Intersection of membership functions 1 2 3 4 5 D1 (x) = 1.00 D2 (x) = 0.17 D3 (x) = 0.89 D4 (x) = 0.22 D5 (x) = 0.78 F1 (x) = 0.23 F2 (x) = 0.13 F3 (x) = 0.50 F4 (x) = 0.25 F4 (x) = 1.00 ıE1 (x) = 0.73 ıE2 (x) = 1.00 ıE3 (x) = 0.90 ıE4 (x) = 0.59 ıE5 (x) = 0.63 min D1 , F1 , E1 = 0.23 min D2 , F2 , E2 = 0.13 min D3 , F3 , E3 = 0.50 min D4 , F4 , E4 = 0.22 min D5 , F5 , E5 = 0.63 Fig. 3. Distribution network. Fig. 4. Distribution network in a substation of AES Sul. the best option is “5”, following of the options “3”, “1”, “4” and “2”, respectively. Thus, after the computer simulations there is a rank for allocation of remotely controlled switches, according to the objective functions defined, without violating the constraints established. So the utility can schedule the investments in the network. 5. Experimental analysis Case studies have been conducted with the AES Sul power utility, in Brazil, in order to verify the applicability of the proposed methodology. The network considered involves the metropolitan area of AES Sul, with ten feeders as shown in Fig. 4 The proposed algorithm takes place by first considering the 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 of the point analyzed can be transferred to other feeders without violating the constraints, the point under consideration can receive a remotely controlled switch. The results obtained for the pair of switches tested are then verified, always considering the gains when comparing the initial configuration and with regard to reduction of reliability indexes (ESAIDI, ESAIFI and EENS). Finally, it is applied the multi-criteria decision making method based on Bellman–Zadeh algorithm for calculate an overall indicator for each pair of switches (NC-NO), corresponding to the respective value of intersection of the membership functions (Eq. (15)). Therefore, the couple of points that present the highest indicator value is considered as the best solu- tion for allocation of the remotely controlled switches. Fig. 5 shows the points selected for the pair of switches that presented the best results. AES Sul has installed in its distribution network one pair of these remotely controlled switches, allocating them in the points indicated by the tool developed that would present the best results. Follows the operation strategy of the switches when there is an outage of the feeder: Fig. 5. Results obtained from the multi-criteria analysis for the allocation of remotely controlled switches. Author's personal copy 420 D.P. Bernardon et al. / Electric Power Systems Research 81 (2011) 414–420 Table 6 Results obtained with the use of remotely controlled switches. Description Before installing the remotely controlled switches After installing the remotely controlled switches Reduction Mean time to restore energy Faults upstream the NC switch Faults downstream the NC switch Clients upstream the switch Clients downstream the switch Clients upstream the switch Clients downstream the switch 1 h36 min 1 h36 min – 48 min 5 min 43 min 32 min 0 min 32 min 1 h43 min 1 h43 min – • Fault downstream of the NC remotely controlled switch: in the event of a fault, the current values of short-circuit will be flagged online in the SCADA (Supervisory Control and Data Acquisition) system. So, it is assumed that the failure occurred downstream of the NC remotely controlled switch; then, the NC remotely controlled switch is operated automatically to isolate the defect. As the time to open the switch, from the time when the fault is detected, is less than 3 min it is considered that the upstream consumers do not suffer any impact due to the failure, for purposes of accounting the reliability index. For the downstream customers, the total time to restore energy is computed. • Fault upstream of the NC remotely controlled switch: in this case, the fault current values of short-circuit will not be flagged in the SCADA system. So, it is assumed that the failure occurred upstream of the NC remotely controlled switch, automatically operating it to open (NC) and then to close the NO one in order to transfer consumers downstream of the NC switch to the adjacent feeder. As the transfer time is on average of 5 min, this time is considered for the consumers transferred, i.e., downstream the switch. For the consumers upstream, the total time to restore energy is computed. Table 6 shows the results obtained by the application of this methodology in case of outage of the feeder when considering the power restoration time. 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 main trunk feeder. 6. Conclusions It was demonstrated that the remotely controlled switches significantly affect the reliability indexes (ESAIDI, ESAIFI and EENS), and the improvement of these indicators is not proportional, with tradeoffs among them. So, the main contributions of this paper are the methodology to consider the impact of remotely controlled switches when computing the reliability indexes and the algorithm for multi-criteria decision making to allocate these 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 electrical distribution utilities. For a better evaluation of the software’s performance, case studies were carried out with actual systems and the results have proved its effectiveness. Acknowledgements The authors would like to thank the technical and financial support of 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). References [1] H.P. Schmidt, N. Ida, N. Kagan, J.C. Guaraldo, Fast reconfiguration of distribution systems considering loss minimization, IEEE Transactions on Power Systems 20 (3) (2005) 1311–1319. [2] R.E. 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Germond, Optimal configuration of electrical distribution networks using heuristic methods, in: Proceedings of the 11th Power Systems Computation Conference, vol. 1, Zurich, 1993, pp. 147–154. [13] D.P. Bernardon, V.J. Garcia, A.S.Q. Ferreira, L.N. Canha, Electric distribution network reconfiguration based on a fuzzy multi-criteria decision-making algorithm, Electric Power Systems Research 79 (2009) 1400–1407. Daniel Pinheiro Bernardon was born in Santa Maria, Brazil on the 15th of September 1977. He got his Dr. Eng. degree from Federal University of Santa Maria, in 2007, and has been a professor of Electrical Engineering at Federal University of Pampa, since 2008. His research interests include distribution system analysis, planning and operation, besides working for ten years in Operation of Electric Systems.