Multi-Objective Allocation of DG Simultaneous with Capacitor and Protective Device Including Load Model

14 Int'l Conf. Artificial Intelligence ICAI'17 Multi-Objective Allocation of DG Simultaneous with Capacitor and Protective Device Including Load Model H. A. Shayanfar *,1, H. Shayeghi 2, M. Alilou 3, R. Dadkhah Doltabadi 4 1 College of Technical & Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran 2 Electrical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran 3 Electrical Engineering Department, University of Urmia, Urmia, Iran 4 Technical and Vocational Institute Razi, Technical and Vocational University, Ardabil, Iran hashayanfar@gmail.com, hshayeghi@gmail.com, masoud.alilou@yahoo.com, r.dolatabadi@gmail.com *Abstract - In this paper, the simultaneous placement of multi- DG, capacitor bank and protective devices are discussed in the distribution system with load model sensitive to voltage and frequency. Moreover, the various customers daily load patterns are considered for evaluation the proposed algorithm in more realistic conditions. Wind turbine, photovoltaic, micro turbine and fuel cell are studied during the study. The proposed multi-objective functions are including of reduction of active and reactive power loss, improvement of voltage and reliability indices. The Hybrid Firefly Algorithm and Particle Swarm Optimization (HFAPSO) is used for multi-objective optimization. Moreover, a method based on fuzzy set theory is employed to extract one of the Pareto-optimal solutions as the best compromise one. The proposed algorithm is tested on a IEEE 69-bus test system and actual 101-bus distribution system in Khoy-Iran. The results indicate high performance the proposed method in improving the technical view point of the network with simultaneous placement of devices. Keywords: Capacitor bank, DG, HFAPSO, Load model, Multi-objective optimization, Protective device. 1 Introduction DG unit is an electric power source connected directly to the distribution network or on the customer site of the matter [1]. DG technologies can be categorized as renewable (wind turbine and photovoltaic) and nonrenewable (micro turbine and fuel cell) [2]. DG units change the direction of power flow and the fault current level of the system; so protection plans of the distribution system become unstable. Therefore, the design of protective devices should be updated based on DG location [3]. Reactive power has a direct effect on the technical indices and performance of the network. So the system operator has special attention to the reactive power beside the active power. The capacitor bank is the usual and cheapest method to compensation the reactive power [4]. The studies of distribution systems that used DG, Capacitor bank and protective device for increasing the efficiency of the network can be divided into four categories. A) Optimal locating and sizing of DG [5-6]. B) Optimal locating and sizing of capacitor bank [7-8]. C) Optimal locating and sizing of protective devices [9-10]. D) Optimal locating and sizing of all considered devices [11-12]. As mentioned above, the different studies about the placement of DG, capacitor and protective devices have been done in the distribution network, but so far study on the simultaneous placement of multi DG, capacitor bank and protective device due to the complexity of the issue has not been done. In this study, the two devices including breaker and sectionaliser are used for increasing the reliability of distribution network. Of course, the sectionaliser is located in upstream of relay because sectionaliser can operate only in the no-load situation; so the order of sectionaliser in this study is the combination of sectionaliser and relay. A breaker as the final protective instrument is placed at the beginning of the feeder and sectionaliser is located on the branch or sub-branch in the system according to the location of DG and capacitor bank so that the maximum stability is obtained in the event of a fault. Of course, the conditions and limitations of network such as bus voltage limits should be evaluated during the creation of island areas. Hence after creating the island region, power flow should be done in this area; if the problem constraints are not violated, the island will continue to work and otherwise island like upstream loads will be interrupted. Therefore in this study, the HFAPSO is used for simultaneous placement of multi DG, capacitor and sectionaliser. After running the intelligent algorithm for multi-objective optimization, a method based on fuzzy set theory is employed to extract one of the Pareto optimal solutions as the best compromise one. DG units including wind turbine, photovoltaic, micro turbine and fuel cell are studied. Optimization the location and size of devices are done in the distribution system with customers daily load patterns and load model sensitive to voltage and frequency. The objectives of the problem are reduction of active and reactive power loss, improvement of voltage index and reliability indices. The proposed method is tested on the IEEE 69-bus test system and actual 101-bus distribution system in Khoy-Iran. Results show high performance of the proposed algorithm in improving the technical indicators of the distribution network. 2 Distributed generation technology A brief description of each used DG types and their model in power flow equations are shown as follow [1, 13]: Wind turbine: Totally, wind turbines consume reactive power to produce active power. A wind turbine in the load flow * Corresponding Author : H.A. Shayanfar, hashayanfar@gmail.com

Int'l Conf. Artificial Intelligence ICAI'17 15 equations is modeled as a PQ bus model with variable reactive power. Photovoltaic: This DG type produces only active power; so, it is modeled as P bus model in the load flow analysis. Nomenclature, Active and reactive power at actual voltage, Active and reactive power at nominal voltage Voltage of bus (Pu) Nominal voltage (1 Pu), Actual and nominal frequency Voltage slop for active and reactive power Frequency slop for active and reactive power,, Loss, Voltage and Reliability Index, Active and reactive loss after installation (MW, Mvar), Active and reactive loss before installation (MW, Mvar), Resistance and reactance of branch Current of branch Branch number Penalty coefficient at loss index equation, Voltage profile and voltage stability after installation, Voltage profile and voltage stability before installation Penalty coefficient Bus number Reliability indices after installation Reliability indices before installation Customer number of bus Failure rate of bus ( ) Average annual unavailability of bus Average load of bus (KW), Minimum and maximum voltage at bus (Pu), Minimum and maximum capacity of DG (MW), Minimum and maximum value of the objective function Number of objective functions Number of non-dominated solutions Micro turbine: Micro turbine has capable of injection both active and reactive power to the network. This kind of DG is modeled as a constant voltage bus model in load flow. Fuel cell: A fuel cell produces only active power; so it is modeled as a P bus model in load flow studies. 3 Load model In this study, the non-linear load model is considered to test the proposed method in more realistic conditions of operation. Load model of the system has been considered as a combination of daily load pattern and sensitive load to voltage and frequency. Practical voltage-frequency dependent load model can be mathematically expressed as the 1 and 2 [14]. (1) (2) The values of dependence coefficients for different sorts of load model are shown in Table 1 [14]. As well as, nominal and actual frequencies are 1 and 0.98 Pu, respectively. The load model of the network is also changed based on customers daily load patterns that the average hourly demand data in Pu is showed in Table 2 [15]. Table 1 Load Types and Values of Dependence Coefficients Type of Load Constant 0 0 0 0 Residential 1.7 2.6 1.0-1.7 Industrial 0.1 0.6 2.6 1.6 Commercial 0.6 2.5 1.5-1.1 Table 2 Load Demand of Each Type of Load Model during a Day (Pu) Type of Hours load 1 2 3 4 5 6 7 8 Constant 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Residential 0.63 0.65 0.60 0.60 0.60 0.60 0.60 0.65 Industrial 0.83 0.83 0.83 0.83 0.83 0.80 0.80 0.86 Commercial 0.60 0.50 0.50 0.50 0.50 0.50 0.50 0.55 9 10 11 12 13 14 15 16 Constant 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Residential 0.63 0.73 0.77 0.77 0.77 0.73 0.68 0.68 Industrial 0.98 1.00 1.00 1.00 0.98 0.94 0.98 0.98 Commercial 0.55 0.65 0.70 0.80 0.85 0.75 0.70 0.65 17 18 19 20 21 22 23 24 Constant 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Residential 0.73 0.80 1.00 1.00 1.00 0.95 0.85 0.75 Industrial 0.94 0.92 0.86 0.83 0.83 0.86 0.83 0.86 Commercial 0.75 0.80 1.00 1.00 0.90 0.80 0.70 0.60 4 Objective functions and constraints Multi objective functions have been defined including loss, voltage and reliability indices. Mathematically, the main objective function is formulated as: (3) It is worth mentioning here that for evaluation various indices of distribution system; firstly, the amount of considered index is calculated during the day according to load model and daily pattern. Secondly, the worst amount of index during the day is considered as amount of index. 4.1 Loss index Loss index as the most important technical index is defined with combination active and reactive power loss as: (4) (5) (6) The functions for calculation active and reactive losses are: (7) (8) 4.2 Voltage index Voltage index including voltage profile and voltage stability is expressed as the 9. (9) (10) (11)

16 Int'l Conf. Artificial Intelligence ICAI'17 4.2.1 Voltage profile This index indicates bus voltage deviation from nominal voltage. Hence, the network performance will be better when the amount of this index is closer to zero. For calculation the voltage profile has been used from the 12. (12) 4.2.2 Voltage stability Voltage stability is the ability of the system to maintain the voltage in acceptable level so that when the nominal load of the system is increased, the delivered active power to the load by the system is increased too; therefore both power and voltage remain in controllable condition. Eq. 13 represents the voltage stability index [16]. (13) 4.3 Reliability index In the distribution system, the reliability is related to consumer power outages and disruption in the performance of equipment [17]. System average interruption frequency index (SAIFI), system average interruption duration index (SAIDI) and average energy not supplied index (AENS) are used as reliability indices of load points. So, the reliability index is defined by Eq. 14. The HFAPSO is a hybrid and intelligent algorithm which has been introduced by Shayeghi and Alilou in Ref. [18]. HFAPSO uses the mechanism of FA and PSO. When the two algorithms are combined, due to global communication between particles in the FA and simple search mechanism with high accuracy in the PSO, the space of particles' motion is limited; Moreover, the probability of algorithm diversion is reduced and also more optimal results in less time than other algorithms are obtained. This hybrid algorithm also has appropriate performance in complex issues [18]. After multi objective optimization, a fuzzy satisfying method, which represents the goals of each objective function, is applied to find the best compromise solution [19]. Totally, to achieve the best result; firstly, objective functions is optimized using the multi-objective HFAPSO. At every stage, according to the location of devices, island regions are determined. After determining island areas, power flow is done for these regions; if the voltage at the buses is not violated the voltage constraints, the island will continue to work and otherwise it will be interrupted. After applying the intelligent algorithm, fuzzy decision-making is used to select the optimal location and size of devices. Using the mentioned method, the complete algorithm for simultaneous placement of instruments in the distribution system is shown in Fig. 1. Start (14) The reliability indices of load points are calculated by [17]. (15) (16) Read data from network Create random particles Run power flow Calculate the objective functions Select the type and number of devices Select the load model Calculate the amount of indices during the day Select the worst amount of technical indices (17) 4.4 Problem constraints There are following constraints during the implementation of the proposed algorithm. 1. The following range of voltage of the buses is allowable: (18) 2. The utilized DG unit must have the allowable size as the following range: (19) 3. Only one sectionaliser can be located in each branch. 5 Proposed method In this study, the hybrid firefly algorithm and particle swarm optimization (HFAPSO) is performed for simultaneous placement of multi DG, capacitor bank and sectionaliser. After multi-objective optimization, a method based on fuzzy set theory is employed to extract one of the Pareto optimal solutions as the best compromise one. Update particles using the multi-objective HFAPSO No Have reached maximum iterations? Yes Run fuzzy decision-making Calculate final amount of indices of distribution system Run power flow Calculate the amount of indices during the day Select the worst amount of technical indices Calculate the objective functions Update iteration counter k=k+1 Select the best particle Save the optimal solution equal to the best particle End Fig. 1 Flowchart of the proposed method for simultaneous placement of multi DG, capacitor bank and sectionaliser

Int'l Conf. Artificial Intelligence ICAI'17 17 Table 3 Simulation parameters Index Value Index Value 0.6 0.4, 0.5, 0.33 0.34, 2 2 0.2-0.95-1.05 (Pu) - 0.1-2 (MW) As regards that nonlinear load model has been considered in this study; firstly, the proposed method is used for selecting the best location and size of multi-dg, capacitor and sectionaliser in each load model including a constant, industrial, commercial and residential model with considering customers daily load patterns and load sensitive to voltage and frequency. Secondly, the optimal location and size of devices in each load model is also evaluated in other load models. Thirdly, the results of systems technical indices in different location and size of devices and load models are compared with each other. Finally, the best location and size of devices are selected, and systems indices are calculated in different load models. 6 Numerical results and discussion In this section, the proposed algorithm for simultaneous placement of multi-dg, capacitor and sectionaliser is applied on IEEE 69-bus radial distribution system and actual 101-bus distribution system in Khoy-Iran. It is assumed that all buses and branches of distribution system have suitable conditions for placement the devices. Moreover, the wind turbine and photovoltaic units are considered as a combination of DG and battery so that they can properly supply the required power during the day. Capacitor banks have a constant capacity between 150 and 1500 Kvar (The steps 150 Kvar). The remained parameters of study are shown in Table 3. 6.1 69-bus distribution system The proposed algorithm was applied to the 69-bus test system to determine the optimal location and size of devices. Firstly, the HFAPSO algorithm was applied to the objective function. Fig. 2 shows sample improvement of the goals or Pareto front simultaneously after applying proposed algorithm. After optimization the objective functions, the fuzzy set theory was used to select the best result. The best location and size of devices in the 69-bus network are shown in Table 4. The 69-bus standard system has 0.2249-MW active and 0.1021-Mvar reactive maximum losses as constant load model; these amounts are changed in different load model. The values of loss index during the placement of devices have been given in Table 5. Fig. 2 Multi-objective optimization by HFAPSO Table 4 Optimal location and size of devices in the 69-bus network Various DG Capacitor Sectionaliser combination (Position: No. bus (Position: No. bus (Position: No. of multi DG / Capacity: MW) / Capacity: Mvar) branch) 1 Wind turbine 42/2 42/1.5 39 2 Photovoltaic 42/2 43/1.5 37 3 Micro turbine 42/2 54/0.6 37 4 Wind turbine 42/1.5 33/0.6 28 Photovoltaic 33/1.5 44/1.5 37 Wind turbine 47/0.755 51/0.6 51 5 Photovoltaic 30/0.289 8/0.45 38 Micro turbine 42/1.77 40/0.45 47 Wind turbine 15/0.430 43/0.75 47 6 Photovoltaic 42/1.5 42/0.75 23 Fuel cell 51/1.134 48/0.75 38 Wind turbine 25/1.407 19/0.3 36 7 Photovoltaic 21/0.1 25/0.45 24 Micro turbine 42/1.750 51/0.45 17 Fuel cell 51/1.325 38/0.3 47 Table 5 Values of loss index in different load model in 69-bus system No. Load model Test Constant Residential Commercial Industrial Initial 0.1758 0.1306 0.1418 0.1522 1 0.0260 0.0261 0.0259 0.0241 2 0.0219 0.0203 0.0207 0.0213 3 0.0170 0.0163 0.0165 0.0166 4 0.0186 0.0183 0.0180 0.0167 5 0.0144 0.0138 0.0140 0.0146 6 0.0141 0.0131 0.0132 0.0140 7 0.0125 0.0129 0.0132 0.0138 In the tests 1 to 3 which only one DG unit has been located beside a capacitor and a sectionaliser, micro turbine has the best performance with 91% reduction of active and 87% reduction of reactive loss. In contrast, wind turbine has weaker performance than photovoltaic and micro turbine with 86 and 82 % reduction of active and reactive loss respectively. The effect of placement of devices by proposed method on the voltage indicators in different load model have been given in Table 6. Evaluation the results of this Table indicates that the amount of voltage profile is reduced and also the amount of voltage stability is increased by placement of devices; therefore network stability is improved. Table 6 Values of voltage indices in different load model in 69-bus system No. Voltage Load model Test index Constant Residential Commercial Industrial Initial 0.0992 0.6833 0.0748 0.7243 0.0818 0.7113 0.0864 0.7024 1 0.0123 0.0111 0.0111 0.0103 0.8927 0.8950 0.8959 0.8994 2 0.0104 0.0095 0.0096 0.0092 0.9004 0.9048 0.9048 0.9082 3 0.0068 0.0062 0.0062 0.0058 0.9097 0.9099 0.9114 0.9157 4 0.0057 0.0050 0.0049 0.0043 0.9126 0.9148 0.9156 0.9189 5 0.0033 0.0029 0.0029 0.0028 0.9232 0.9231 0.9247 0.9292 6 0.0015 0.0015 0.0015 0.0015 0.9202 0.9208 0.9222 0.9263 7 0.0007 0.0007 0.0007 0.0006 0.9296 0.9291 0.9308 0.9355

18 Int'l Conf. Artificial Intelligence ICAI'17 Table 7 The amount of reliability indices after installation of devices SAIFI SAIDI AENS Test 1 0.76139 0.47030 0.10594 Test 2 0.76139 0.47030 0.10594 Test 3 0.76139 0.47030 0.10594 Test 4 0.61739 0.39788 0.08933 Test 5 0.53756 0.35838 0.07984 Test 6 0.49158 0.33480 0.07450 Test 7 0.43873 0.28387 0.06262 Reliability index is one the most important subjects for evaluating the performance of distributions system. In this study, reliability index has been defined as a combination of SAIFI, SAIDI and AENS which they are equal to 2.234, 1.8163 and 0.3997 in the initial state, respectively. The amounts of these indices after installation of devices in the distribution system have been given in Table 7. This results show that proposed algorithm has proper performance especially when the number of devices is increased and the problem becomes more complex. According to above analyses can be concluded that located devices in Fig. 3 (Test 7) are the optimal location and size of devices. Fig. 3 The 69-bus system with optimal devices which have been selected by the proposed algorithm (B: Breaker, S: Sectionaliser, C: Capacitor) 6.2 Actual 101-bus system Another system is a practical system of Khoy Distribution Company in Iran which is shown in Fig. 4. The required information of have been obtained by field studies. The actual network is a 20-KV industrial distribution system which has two feeders, so the load model of industrial is considered in this system. The best location and size of devices in the actual distribution system are shown in Table 8. Fig. 4 Actual 101-bus distribution system in Khoy-Iran Table 8 Optimal size and location of devices in the actual system Various DG Capacitor Sectionaliser combination (Position: No. bus (Position: No. bus (Position: No. of multi DG / Capacity: MW) / Capacity: Mvar) branch) 1 Wind turbine 90/2000 90/1500 64 2 Photovoltaic 90/2000 75/1500 64 3 Micro turbine 90/2000 40/1500 64 4 Wind turbine 91/1857.1 43/1500 17 Photovoltaic 38/2000 78/1500 64 Wind turbine 35/2000 44/1500 10 5 Photovoltaic 72/2000 78/1050 64 Micro turbine 83/2000 65/450 83 Wind turbine 64/1614.7 43/1050 14 6 Photovoltaic 88/2000 88/1500 86 Fuel cell 29/2000 74/1200 64 Wind turbine 70/2000 42/900 40 7 Photovoltaic 91/2000 19/750 14 Micro turbine 22/2000 74/450 85 Fuel cell 41/907.94 86/1500 64 The values of loss and voltage indices during the placement of devices are given in Table 9. The amount of loss index has been reduced about 33-73 percent by placement of devices with proposed method. In installation only one number of devices (Test 1 to 3), the micro turbine and wind turbine are the best and the weakest performance, respectively. The initial values of the voltage profile and stability are 0.0186 and 0.9180, respectively. The information of system s voltage in Table 9 shows that proposed algorithm reduces the amount of voltage profile, and also voltage stability is increased; so it can be claimed that network stability has been improved. According to the results can be said that the placement of 3 or 4 number of various type of DG units with capacitors (Tests 5 to 7) have the greatest effect on the voltage indices. The amount of reliability indices such as AENS in an industrial network is further than other load model networks because the large amounts of power is usually consumed by a few numbers of subscribers in an industrial system. The amounts of reliability indices have been given in Table 10. Table 9 Values of loss and voltage indices of 101-bus system No. Loss indices Voltage indices (Pu) Test Active (MW) Reactive (Mvar) V. Profile V. Stability Initial 0.1779 0.1043 0.0186 0.9180 1 0.1195 0.0700 0.0099 0.9406 2 0.1164 0.0682 0.0095 0.6406 3 0.1006 0.0590 0.0080 0.9443 4 0.0795 0.0466 0.0073 0.9437 5 0.0623 0.0365 0.0029 0.9491 6 0.0616 0.0361 0.0037 0.9479 7 0.0483 0.0283 0.0024 0.9477 Table 10 The amount of reliability indices of 101-bus network SAIFI SAIDI AENS Initial 8.0572 9.4270 1103.85 Test 1 6.1848 7.2362 888.9693 Test 2 6.1848 7.2362 888.9693 Test 3 6.1848 7.2362 888.9693 Test 4 5.1069 5.9751 586.6685 Test 5 4.6663 5.4596 519.0357 Test 6 4.5620 5.3375 517.3003 Test 7 3.1446 3.6791 383.7978

Int'l Conf. Artificial Intelligence ICAI'17 19 400 350 300 250 200 150 100 50 0 Fig. 5 The amount of reliability index by placement of different devices Fig. 6 The actual distribution system with optimal devices which have been selected by proposed method As mentioned above, reliability index is the most important goal in the actual system. The rate of reliability of the actual system is increased about 20-65 percent by located devices. This claim can be shown clearly in Fig. 5. In tests 1-3 that only one number of devices have been located by proposed algorithm, the amount of indices are similar. It means that a capacitor which was located in the system according to the type of DG unit has an appropriate performance because some DG units can t alone support the created island. Other tests also have a suitable performance in the reliability aspects. Consequently, the reliability and stability of the actual network are improved considerably by proposed method. According to analyses of results can be concluded that located devices in figure 6 (Test 7) are the optimal location and size of devices in the 101-bus system. The network will be divided into six protection areas after placement of devices. This new protection plan causes that when a fault occurs, the least possible part of the system will be disconnected. Totally, the reliability of the system is improved about 65 percent by these located devices. Moreover, DG units and capacitors have beneficial effects on the other technical indices; so system efficiency is increased too. 7 Conclusions 19.53% 19.53% 19.53% 46.70% 52.82% 53.99% 65.17% Initial Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 In this paper, simultaneous placement and sizing of the capacitor, sectionaliser and four kinds of DG units have been studied as the multi-objective optimization problem. The load model of distribution system was considered as sensitive to voltage and frequency. Moreover, the various customers daily load patterns were considered for evaluation the proposed algorithm in more realistic conditions. The objectives of the problem were reduction of power loss index, improvement of voltage and reliability indices. The HFAPSO algorithm was used to optimize the objective functions; then, fuzzy decisionmaking was applied to select the best result. For evaluation the proposed method, it has been implemented on IEEE 69-bus and actual 101-bus radial distribution systems. From simulation results, following points can be concluded: 1. The combination of capacitor and all types of DG improves the power loss and voltage indices. Moreover, the reliability indices are also improved when a protective device is located in the optimal place. 2. DG and capacitor are commonly located near the load center, which improve the stability of island areas; on the other hand, technical indices of the network are improved by reducing the load density of lines. Moreover, sectionalisers are located near the supply resources so that the reliability of system increases by creating islanded regions. 3. All kinds of DG have a useful effect on the amount of technical indices, while micro turbine and wind turbine have the greatest and least effect, respectively. The difference of performances depends on the type of output of DG units. 4. The proposed algorithm has practical performance in the various load models and affects the amount of loss and voltage indices so that their change become more linear during the change of load model. This substantial reduction causes that if load model of some buses changes to another model, network efficiency will not change much. References [1] R. Viral, D.K. Khatod, ''Optimal Planning of Distributed Generation Systems in Distribution System: A Review'', Renewable and Sustainable Energy Reviews, Vol. 16, pp. 5146 5165, 2012. [2] D. Q. Hung, N. Mithulananthan, R. C. Bansal, ''Analytical Expressions for DG Allocation in Primary Distribution Networks'', IEEE Transaction and Energy Conversion, Vol. 25, pp. 814-820, 2010. [3] S. A. M. Javadian, M. R. Haghifam, M. Fotuhi Firoozabad, S. M. T. Bathaee, ''Analysis of Protection System s Risk in Distribution Networks with DG'', Electrical Power and Energy Systems, Vol. 44, pp. 688 695, 2013. [4] M. Aman, G. Jasmon, A. Bakar, H. Mokhlis, ''Optimal Shunt Capacitor Placement in Distribution System-A Review and Comparative Study'', Renewable and Sustainable Energy Reviews, Vol. 30, pp. 429-439, 2014. [5] M. H. Moradi, M. Abedini, ''A Combination of Genetic Algorithm and Particle Swarm Optimization for Optimal DG Location and Sizing in Distribution Systems'', Electrical Power and Energy Systems, Vol. 34, pp. 66-74, 2012. [6] S. Kansal, V. Kumar, B. Tyagi, ''Optimal Placement of Different Type of DG Sources in Distribution Networks'', Electrical Power and Energy System, vol. 23, pp. 752-760, 2013. [7] H. Shayeghi, M. Karimi, P. Farhadi, ''Solving Optimal Capacitor Placement Problem Using Improved Bi-Strategy Differential Evolutionary Algorithm Considering Varying Load Conditions'',

20 Int'l Conf. Artificial Intelligence ICAI'17 International Review of Electrical Engineering, Vol. 7, pp. 4092-4104, 2012. [8] S. Taher, R. Bagherpour, ''A New Approach for Optimal Capacitor Placement and Sizing in Unbalanced Distorted Distribution Systems using Hybrid Honey Bee Colony Algorithm'', Electrical Power and Energy System, Vol. 49, pp. 430-480, 2013. [9] A. Moradi, M. Fotuhi-Firuzabad, ''Optimal Switch Placement in Distribution Systems Using Trinary Particle Swarm Optimization Algorithm'', IEEE Transactions on Power Delivery, vol. 23, pp. 271-279, 2008. [10] I. Goroohi Sardou, M. Banejad, R. Hooshmand, A. Dastfan, ''Modified Shuffled Frog Leaping Algorithm for Optimal Switch Placement in Distribution Automation System using a Multi- Objective Fuzzy Approach'', IET Generation, Transmission & Distribution, Vol. 6, pp. 493-502, 2012. [11] M. Raoofat, ''Simultaneous Allocation of DGs and Remote Controllable Switches in Distribution Networks Considering Multilevel Load Model'', Electrical Power and Energy Systems, Vol. 33, pp. 1429-1436, 2011. [12] S. M. Sajjadi, M. R. Haghifam, T. Salehi, ''Simultaneous Placement of Distributed Generation and Capacitors in Distribution Networks Considering Voltage Stability Index'', Electrical Power and Energy System, Vol.46, pp. 366-375, 2013. [13] Ch. Yammani, S. MaheswaraPu, S. Matam, ''Optimal Placement of Multi DGs in Distribution System with Considering the DG bus Available Limits'', Energy and Power, Vol. 2, pp. 18-23, 2012. [14] IEEE Task Force on Load Representation for Dynamic Performance, "Bibliography on Load Models for Power Flow and Dynamic Performance Simulation", IEEE Transaction and Power Systems, Vol. 10, pp. 523-538, 1995. [15] R. Ebrahimi, M. Ehsan, H. Nouri, "A Profit-Centric Strategy for Distributed Generation Planning Considering Time Varying Voltage Dependent Load Demand", Electrical Power and Energy Systems, Vol. 44, pp. 168-178, 2013. [16] S. M. Sajjadi, M. R. Haghifam, J. Salehi, "Simultaneous Placement of Distributed Generation and Capacitors in Distribution Networks Considering Voltage Stability Index", Electrical Power and Energy Systems, Vol. 46, pp. 366-375, 2013. [17] R. Billinton, R. N. Allan, "Reliability evaluation of engineering systems: concepts and techniques", Plenum Publishing, 2nd Edn, 1992. [18] H. Shayeghi, M. Alilou, "Application of Multi Objective HFAPSO Algorithm for Simultaneous Placement of DG, Capacitor and Protective Device in Radial Distribution Network", Journal of Operation and Automation in Power Engineering, Vol. 3, pp. 131-146, 2015. [19] H. Shayeghi, A. Ghasemi, "A Multi Objective Vector Evaluated Improved Honey Bee Mating Optimization for Optimal and Robust Design of Power System Stabilizers", Electrical Power and Energy Systems, Vol. 62, pp.630-645, 2014. Biographies Heidar Ali Shayanfar received the B.S. and M.S.E. degrees in electrical engineering in 1973 and 1979, respectively. He received the Ph.D. degree in electrical engineering from Michigan State University, East Lansing, MI, USA, in 1981. Currently, he is a Full Professor with the Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran. His research interests are in the application of artificial intelligence to power system control design, dynamic load modeling, power system observability studies, voltage collapse, congestion management in a restructured power system, reliability improvement in distribution systems, smart grids and reactive pricing in deregulated power systems. He has published more than 530 technical papers in the international journals and conferences proceedings. Dr. Shayanfar is a member of the Iranian Association of Electrical and Electronic Engineers. Hossein Shayeghi received the B.S. and M.S.E. degrees in Electrical and Control Engineering in 1996 and 1998, respectively. He re ceived his Ph.D. degree in Electrical Engineering from Iran University of Science and Technology, Tehran, Iran in 2006. Currently, he is a full Professor in Technical Engineering Department of University of Mohaghegh Ardabili, Ardabil, Iran. His research interests are in the application of robust control, artificial intelligence and heuristic optimization methods to power system control design, operation and planning and power system restructuring. He has authored and co-authored of 5 books in Electrical Engineering area all in Farsi, one book and two book chapters in international publishers and more than 350 papers in international journals and conference proceedings. Also, he collaborates with several international journals as reviewer boards and works as editorial committee of three international journals. He has served on several other committees and panels in governmental, industrial, and technical conferences. He was selected as distinguished researcher of the University of Mohaghegh Ardabili several times. In 2007 and 2010 he was also elected as distinguished researcher in engineering field in Ardabil province of Iran. Furthermore, he has been included in the Thomson Reuters list of the top one percent of most-cited technical Engineering scientists in 2015 and 2016, respectively. Also, he is a member of Iranian Association of Electrical and Electronic Engineers (IAEEE) and Senior member of IEEE. Rashid Dadkhah Doltabad Received the B.S. Degree in Electrical Engineering from Mazandaran University, Babol Branch. and the M.S.E. degree in the Electrical Engineering Department of Islamic AzadUniversity, Ardabil Branch. His areas of interest in research are Power System Operation.