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A Real Distribution Network Voltage Regulation Incorporating Auto-Tap-Changer Pole Transformer Multiobjective Optimization

A Real Distribution Network Voltage Regulation Incorporating Auto-Tap-Changer Pole Transformer... applied sciences Article A Real Distribution Network Voltage Regulation Incorporating Auto-Tap-Changer Pole Transformer Multiobjective Optimization 1 , 2 1 3 Sayed Mir Shah Danish * , Ryuto Shigenobu , Mitsunaga Kinjo , Paras Mandal , 4 5 1 Narayanan Krishna , Ashraf Mohamed Hemeida and Tomonobu Senjyu Faculty of Engineering, University of the Ryukyus, 1 Senbaru Nishihara-cho, Nakagami, Okinawa 903-0213, Japan Department of Electrical and Electronics Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui-shi, Fukui 910-8507, Japan Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX 79968, USA Department of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur, Tamil Nadu 613401, India Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt * Correspondence: sayedmir.danish@gmail.com Received: 11 June 2019; Accepted: 11 July 2019; Published: 14 July 2019 Abstract: A number of studies realized operation of power systems are unstable in developing countries due to misconfiguration of distribution systems, limited power transfer capability, inconsistency of renewable resources integration, paucity of control and protection measures, timeworn technologies, and disproportionately topology. This study underlines an Afghanistan case study with 40% power losses that is mainly pertinent from old distribution systems. The long length of distribution systems, low-power transfer capability, insucient control and protection strategy, peak-demand elimination, and unstable operation (low energy quality and excessive voltage deviations) are perceived pre-eminent challenges of Afghanistan distribution systems. Some attainable solutions that fit challenges are remodeling (network reduction), networks reinforcement, optimum compensation strategy, reconfiguration options, improving, and transfer capability. This paper attempts to propose a viable solution using multiobjective optimization method of auto-tap-changer pole transformer (ATCTr). The proposed methodology in terms of optimal numbers and placement of ATCTr can be known as a novel two-dimensional solution. For this purpose, a real case of Kabul City distribution system is evaluated. Simulation results indicate the e ectiveness of the proposed method in reducing system losses and improving system overall performance. This approach tends to regulate the voltage deviation in a proper and statutory range with minimum number and optimum placement of ATCTrs. The proposed method is simulated using MATLAB environment to compare and evaluate performance of the proposed network under di erent situations and scenarios. Keywords: auto-tap-changer pole transformer (ATCTr); distribution network; genetic algorithm (GA); multiobjective optimization; voltage deviation control; voltage regulation; voltage stability 1. Introduction Electric power distribution system with multifarious topologies, configurations, and characteristics is one of the salient components of a power system. In most developing countries, increasing demand for electrical energy enforces distribution systems for an increasingly expansion and broadening. For any expansion, power energy quality and eciency require special attention of control, improvement, Appl. Sci. 2019, 9, 2813; doi:10.3390/app9142813 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 2813 2 of 13 and management. One of the most e ective factors in an electric power energy quality is voltage deviation and stability. Extension of a network length and expansion of topology can be associated with the risk of statutory and standard limit [1]. Kabul is a densely populated and capital city of Afghanistan that distribution networks su er unstable-rated operation. These networks are extended without length limitation consideration, which demonstrates unstable voltage beyond the statutory range with huge technical and economic losses. In recent years, the government of Afghanistan bounded to retain environmental protection and sustainable development in accordance with the Paris Agreement 2015 (combat climate change), and Sustainable Development Goals (SDGs) 2030. Reform of the energy sector has been part of this endeavor. Afghanistan’s distribution networks are the least developed and old-fashioned part of the power system. In addition to the technical and financial losses, shortage of access to electric energy has led to increased utilization of primary energy resources and fossil fuel with high environmental impact. Meanwhile, distribution systems to remote areas are extended without expandability capacity (in local and regional networks) consideration. In priority, it must seriously consider and adopt appropriate solutions. The e ective delivery of power to the end users can be achieved by improving reliability, eciency, cost-e ectiveness, and sustainability measures of production and distribution [2]. Various investigations using di erent optimization techniques, methods, and solutions are conducted to regulate voltage and reduce energy losses in a distribution system. In [3], multiobjective optimization of auto-tap-changer pole transformer with respect to minimizing the voltage deviation of a 16-bus distribution network was tested. In [4], a coordinated control of distributed energy storage system (ESS) with traditional voltage regulators including on-load tap changer transformers (OLTC) and step voltage regulators (SVR) was applied. Authors of [5] proposed data fusion theory to develop a comparative diagnostic method to determine the operation status of on-load tap changers mechanism. A study was carried out in [6] to enhance power quality with automatic tap change in transformer in a smart grid distribution system. In [7], an implementation of a prototype electronic tap-changer instead of mechanical tap-changer was proposed. This method was demonstrated with some shortcomings, such as low operating speed, short lifetime, and heavy size. In [8], the authors employed a nonlinear dynamic model of OLTC, impedance loads, and decoupled reactive power voltage relations to reconstruct the voltage collapse phenomenon. This method aims to determine operation status of on-load tap changers mechanism. Likewise, in [9], a network reconfiguration was carried out over two domains simultaneously: Re-switching strategies and transformer tap-changer adjustments. Similarly, several techniques and strategies for voltage stability enhancement and regulation have been applied, using several case studies under di erent conditions [10–15]. This study aims to present a fully solid-state tap-changer solution with a new control strategy and optimal configuration. Over the past decades, power system blackouts due to voltage instability were repeatedly reported; namely, Tokyo blackout on 23 July 1987 and United Kingdom, Sweden, Canada, Denmark, Italy, and the United States blackouts in 2003 [16]. Power system voltage stability has been discussed enough over the past decades. In [17], a control strategy for reactive power compensation using storage system was studied. This study aimed to improve system stability by a proper prediction of reactive behavior and demand for di erent operation conditions. In [18], the authors presented the stability analysis using the load and generation levels as a direction vector for the base system through continuation power flow (CPF) under normal condition and contingency. The authors of [19] proposed a wavelet transform (WT) based on data analysis to extract the features from real-time active power and RMS (root mean square) voltage of the power grid. This study applied a hybrid classification technique based on particle swarm optimization (PSO) and support vector machines (SVM) to classify the features and diagnose di erent types of faults in a smart grid system. Previous studies investigated the use of control devices in a variety of ways based on di erent optimization methods. Most of these studies were focused on required number of control device without considering the optimum placement and number of these devices. The proposed methods can technically be feasible, but economically they are not acceptable. Therefore, reducing number of control Appl. Sci. 2019, 9, x 3 of 12 Previous studies investigated the use of control devices in a variety of ways based on different optimization methods. Most of these studies were focused on required number of control device without considering the optimum placement and number of these devices. The proposed methods Appl. Sci. 2019, 9, 2813 3 of 13 can technically be feasible, but economically they are not acceptable. Therefore, reducing number of control devices in a system is another important objective. To solve the trade-off problem, the devices in a system is another important objective. To solve the trade-o problem, the multiobjective multiobjective optimization is an excellent tool. Additionally, the increase in tap position changing optimization is an excellent tool. Additionally, the increase in tap position changing can reduce contacts can reduce contacts lifespan and accelerate deterioration of transformer oil in the switching process lifespan and accelerate deterioration of transformer oil in the switching process [20]. This paper [20]. This paper provides a method of multiobjective optimization of auto-tap-changer pole provides a method of multiobjective optimization of auto-tap-changer pole transformer (ATCTr), in term transformer (ATCTr), in term of optimum number and placement of tap position changes. of optimum number and placement of tap position changes. Meanwhile, a multiobjective optimization Meanwhile, a multiobjective optimization using genetic algorithm [21–25] is applied to minimize using genetic algorithm [21–25] is applied to minimize voltage deviation. In Section 2, characteristics voltage deviation. In Section 2, characteristics of system model and problem description are of system model and problem description are discussed. Section 3 presents the methodology, follows discussed. Section 3 presents the methodology, follows by the simulation result and comparison in by the simulation result and comparison in Section 4. At last, Section 5 concludes simulations findings Section 4. At last, Section 5 concludes simulations findings and briefs novelty and effectiveness of the and briefs novelty and e ectiveness of the study. study. 2. Characteristics of the System Model and Problem Description 2. Characteristics of the System Model and Problem Description The targeted model in this study was located in Kabul city (capital of Afghanistan). Triple energy The targeted model in this study was located in Kabul city (capital of Afghanistan). Triple energy sectors, generation, transmission, and distribution systems, su er technical and economic losses. sectors, generation, transmission, and distribution systems, suffer technical and economic losses. After a long-term political instability and lack of maintenances, Kabul city distribution networks After a long-term political instability and lack of maintenances, Kabul city distribution networks demonstrate many problems; namely, transformer no-load loss, imbalance between primary and demonstrate many problems; namely, transformer no-load loss, imbalance between primary and secondary distribution systems in terms of power transfer, scattered distribution transformer from secondary distribution systems in terms of power transfer, scattered distribution transformer from gravity center of load, unbalance reactive power and distributed three phase supply, lack of protection gravity center of load, unbalance reactive power and distributed three phase supply, lack of devices, long length of customers cables, use of nonstandard equipment, etc. [26]. Reports pertain protection devices, long length of customers cables, use of nonstandard equipment, etc. [26]. Reports 25–40% losses to distribution systems that require a viable solution and proper management of technical pertain 25–40% losses to distribution systems that require a viable solution and proper management and economic losses [27]. Meanwhile, an increasing population growth forces distribution networks to of technical and economic losses [27]. Meanwhile, an increasing population growth forces operate close to their stability limit within maximum expandability [28]. Definitely, system expansion distribution networks to operate close to their stability limit within maximum expandability [28]. under stressed voltage control condition has a direct impact on voltage profile and power losses [29]. Definitely, system expansion under stressed voltage control condition has a direct impact on voltage For this case study, voltage deviation at distribution level is out of acceptable range; whereas, at the profile and power losses [29]. For this case study, voltage deviation at distribution level is out of time of peak load demand, it reaches 15% voltage deviation. acceptable range; whereas, at the time of peak load demand, it reaches 15% voltage deviation. Figure 1 shows the proposed 20 kV distribution system consisting of 22 buses and 21 lines that Figure 1 shows the proposed 20 kV distribution system consisting of 22 buses and 21 lines that are considered a real model of simulation. Table 1 illustrates the mentioned distribution system are considered a real model of simulation. Table 1 illustrates the mentioned distribution system transmission lines parameters. The proposed model supplies residential, commercial, and industrial transmission lines parameters. The proposed model supplies residential, commercial, and industrial consumers. This system consists of transformer stations (TSs) and junction station (JS-6) that feeds consumers. This system consists of transformer stations (TSs) and junction station (JS-6) that feeds from the (110/20 KV, 50 MVA Breshna Kot substation). from the (110/20 KV, 50 MVA Breshna Kot substation). Figure Figure 1. 1. Bresh Breshna na Kot distribu Kot distribution tion network network model. model. Table 1. Kabul city 20 kV distribution system transmission line parameters. Bus Code Line Number Length (km) R (pu) X (pu) From To 1 1 2 0.75 0.246 0.072375 2 2 3 0.8 0.2624 0.0772 Appl. Sci. 2019, 9, 2813 4 of 13 Table 1. Kabul city 20 kV distribution system transmission line parameters. Bus Code Length (km) R (pu) X (pu) Line Number From To 1 1 2 0.75 0.246 0.072375 2 2 3 0.8 0.2624 0.0772 3 3 4 0.6 0.1968 0.0579 4 3 12 0.4 0.1312 0.0386 5 4 5 0.65 0.2132 0.062725 6 5 6 0.95 0.3116 0.091675 7 5 13 0.7 0.2296 0.06755 8 6 7 0.65 0.2132 0.062725 9 6 14 1.4 0.4592 0.1351 10 14 15 0.6 0.1968 0.0579 11 7 8 0.8 0.2624 0.0772 12 7 16 0.65 0.2132 0.062725 13 16 17 0.6 0.1968 0.0579 14 17 18 0.55 0.1804 0.053075 15 8 9 0.65 0.2132 0.062725 16 9 10 0.4 0.1312 0.0386 17 9 19 0.8 0.2624 0.0772 18 19 20 0.45 0.1476 0.043425 19 20 21 0.4 0.1312 0.0386 20 21 22 0.4 0.1312 0.0386 21 10 11 0.45 0.1476 0.043425 3. Methodology Maintaining stable operation and reliable supply remain the first ever anticipation of any distribution system [30]. The e ectiveness of voltage control device over available approaches for voltage stability and control are highlighted in the literature. This study targets ATCTr from di erent standpoints of optimum selection, requirement, and placement. Proper planning of ATCTr contributes voltage stability and improve voltage profile with minimum number of control devices. Since ATCTr devices are expenses, considering the minimum penetration of these devices with optimum placement can optimize resources technically and economically (installation and maintenances costs). This paper deals with optimum required number and placement of ATCTr using multiobjective algorithm. 3.1. Multiobjective Optimization Using Genetic Algorithm Multiobjective formulations are realistic models for many complex engineering optimization problems. A reasonable solution to a multiobjective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. Multiobjective optimization using genetic algorithm (GA) is approached in this paper to obtain the optimal number of ATCTr, and minimize voltage deviation [21–25]. Load flow analysis is simulated by Newton–Raphson (NR) method [31]. The current distribution network is considered as a single-phase model, operating under a balanced state. 3.2. Objective Functions Selection of the objective functions is a significant task to obtain an optimum solution in an optimization problem. It also necessarily a ects optimization behavior as well. In this study, two objective functions are considered for optimization as shown in Equations (1) and (2). min : F = a (1) 1 i i=1 Appl. Sci. 2019, 9, 2813 5 of 13 min : F = (V 1) (2) 2 i,t i=1 where, F is the objective function, it represents the total number of installed ATCTr, and F is another 1 2 objective function represents overall voltage deviation of nodes. a represents the number of introduced ATCTr at each node i, V is the voltage deviation on each node i at time t, N is the total number i,t of nodes. Constraint inequalities are as follows: V  V  V (3) min i max T  T  T (4) min i max where, V is the distribution voltage of node i; V , V are voltage lower and upper limits, respectively. i min max T is the tap position of node i; T , T are the tap position lower limit and tap position upper max i min limit, respectively. Equality restriction is as follows: g : x = 5 (5) A t t=0 g : x = 10 (6) B t t=0 g : x = 15 (7) t=0 where g –g are the constraints of the number of tap change position, and x is the tap change position A C number at time t. 3.3. Optimal Placement Problem Optimal placement problem of control devices remains a serious issue. Sometimes, disarrangement of control device not only cannot be e ective, also can be associated with technical and economic losses. Likewise, if equipment is not fit in an optimal location, its e ectiveness decreases and is not technically feasible. When the objective function is set to minimize voltage deviation and number of installed ATCTr, in this scenario, voltage control eciency depends on the placement of ATCTr [32,33]. Moreover, optimal scheduling of devices depends on the placement of the devices. Therefore, optimum placement of ATCTrs can reduce voltage deviation. Multiobjective optimization using GA with the objective function of voltage deviation was applied to solve the optimization problem. The proposed method aims to hence perform a power flow analysis, to calculate voltage magnitudes at di erent buses. GA randomly in each process locates ATCTr in di erent nodes with di erent alignments and configurations. These processes are repeatedly carried out until the comparison between all genes is made. Finally, the best gene (optimal placement) with least voltage deviation is specified from comparing the last population with the best gene from the new population. In order to take into account, the optimal placement of ATCTrs, a string of N bits (representing the total N nodes) was used to decide the location nodes at which to introduce an ATCTr, as shown below: P = (a , a , : : : , a ), (a 2 1, 0) (8) N N1 1 i where, P represents the placement of installed ATCTrs in overall nodes. Here, “0” represents a node with no ATCTr, whereas “1” represents a node with ATCTr-installed bus. Figure 2 and Table 2 represent an example of coding used for multiobjective optimization; the placement of installed ATCTrs in distribution network is demarcated with circles. Appl. Sci. 2019, 9, 2813 6 of 13 Appl. Sci. 2019, 9, x 6 of 12 Appl. Sci. 2019, 9, x 6 of 12 Figure 2. Figure 2. An ex An example ample of auto-t of auto-tap-changer ap-changer pole transfor pole transformer mer ((A ATCTr) installation placement cording. TCTr) installation placement cording. Figure 2. An example of auto-tap-changer pole transformer (ATCTr) installation placement cording. Table 2. Binary coding use for ATCTrs placement in nodes. Table 2. Binary coding use for ATCTrs placement in nodes. Table 2. Binary coding use for ATCTrs placement in nodes. Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Installed position 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Installed position 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Installed position 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Comprehensively, the stages of the proposed methodology are demonstrated in the flowchart Comprehensively, the stages of the proposed methodology are demonstrated in the flowchart Comprehensively, the stages of the proposed methodology are demonstrated in the flowchart shown in Figure 3. shown in Figure 3. shown in Figure 3. Figure 3. Figure 3. Flow Flowchart chart of the of the multiobject multiobjective ive opt optimization imization of of A ATCT TCTrs. rs. Figure 3. Flowchart of the multiobjective optimization of ATCTrs. 4. Simulation Result and Comparison 4. Simulation Result and Comparison 4. Simulation Result and Comparison To confirm the e ectiveness of introducing ATCTr (with a provision of10% change in voltage at To confirm the effectiveness of introducing ATCTr (with a provision of ±10% change in voltage To confirm the effectiveness of introducing ATCTr (with a provision of ±10% change in voltage 1.25% additional voltage per tap), simulation results based on the physical structure of the current at 1.25% additional voltage per tap), simulation results based on the physical structure of the current at 1.25% additional voltage per tap), simulation results based on the physical structure of the current distribution system is shown in Figure 1. distribution system is shown in Figure 1. distribution system is shown in Figure 1. Proper range for voltage deviation as defined by standard is 0.95  V  1.05 pu. The proposed Proper range for voltage deviation as defined by standard is 0.95 ≤ 𝑉 ≤1.05 pu. The proposed Proper range for voltage deviation as defined by standard is 0.95 ≤ 𝑉 ≤1.05 pu. The proposed distribution network parameters considering daily load profile and real-time voltage profile of the distribution network parameters considering daily load profile and real-time voltage profile of the distribution network parameters considering daily load profile and real-time voltage profile of the entire system are plotted in Figure 4a,b, respectively. This is followed by the distribution voltage entire system are plotted in Figure 4a and 4b, respectively. This is followed by the distribution voltage entire system are plotted in Figure 4a and 4b, respectively. This is followed by the distribution voltage magnitude using ATCTrs in Figure 4c. The number of tap position changes in a 24-h period (g ) is magnitude using ATCTrs in Figure 4c. The number of tap position changes in a 24-h period (𝑔 ) is 15 magnitude using ATCTrs in Figure 4c. The number of tap position changes in a 24-h period (𝑔 ) is 15 15 times. Moreover, the Pareto optimum solution for minimizing the number of introduced ATCTrs times. Moreover, the Pareto optimum solution for minimizing the number of introduced ATCTrs and times. Moreover, the Pareto optimum solution for minimizing the number of introduced ATCTrs and and minimizing of the voltage deviation considering tap position changes is shown in Figure 4d. minimizing of the voltage deviation considering tap position changes is shown in Figure 4d. minimizing of the voltage deviation considering tap position changes is shown in Figure 4d. Appl. Sci. 2019, 9, 2813 7 of 13 Appl. Sci. 2019, 9, x 7 of 12 (a) (b) (c) (d) Figure 4. Real distribution network model: (a) Daily load profile; (b) hourly voltage profile (uncontrolled); Figure 4. Real distribution network model: (a) Daily load profile; (b) hourly voltage profile (c) voltage profile using ATCTrs (controlled); (d) Pareto optimal solutions. (uncontrolled); (c) voltage profile using ATCTrs (controlled); (d) Pareto optimal solutions. Genetic algorithm (GA) as a multiobjective optimization technique is used to obtain study Genetic algorithm (GA) as a multiobjective optimization technique is used to obtain study objectives. Since the number of tap position changes is representative of a rough equipment lifetime, objectives. Since the number of tap position changes is representative of a rough equipment lifetime, multiobjective optimization was solved for tap position using the number of tap position changes as multiobjective optimization was solved for tap position using the number of tap position changes as a parameter (g –g ). Tables 3–6 show the location of ATCTrs for the solutions A–D. Pareto optimal A C a parameter (𝑔 –𝑔 ). Tables 3–6 show the location of ATCTrs for the solutions A–D. Pareto optimal solutions are shown in Figure 4d. solutions are shown in Figure 4d. Table 3. Optimum placement of ATCTrs (solution A). Table 3. Optimum placement of ATCTrs (solution A). Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Equality constraint Equality 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 of g constraint of gA Equality Equality constraint 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 constraint of gB of g Equality Equality constraint0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 constraint of gC 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 of g Table 4. Optimum placement of ATCTrs (solution B). Table 4. Optimum placement of ATCTrs (solution B). Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Equality 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 constraint of gA Equality constraint 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 Equality of g 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 constraint of gB Equality constraint Equality 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 of g constraint of gC Equality constraint 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 of g Table 5. Optimum placement of ATCTrs (solution C). Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Equality 0 0 1 0 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 0 1 1 constraint of gA Equality 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 0 1 0 1 constraint of gB Equality 0 1 0 1 0 1 1 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 constraint of gC Appl. Sci. 2019, 9, 2813 8 of 13 Table 5. Optimum placement of ATCTrs (solution C). Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Equality constraint 0 0 1 0 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 0 1 1 of g Equality constraint 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 0 1 0 1 of g Equality constraint 0 1 0 1 0 1 1 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 of g Table 6. Optimum placement of ATCTrs (solution D). Appl. Sci. 2019, 9, x 8 of 12 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Table 6. Optimum placement of ATCTrs (solution D). Equality constraint 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 of g Equality Equality constraint1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 constraint of gA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 of g Equality 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Equality constraint constraint of gB 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 of g Equality 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 constraint of gC The voltage waveforms for solutions A–D has shown in Figures 5–8. The voltage waveforms for solutions A–D has shown in Figures 5–8. (a) (b) (c) Figure 5. Node voltages of solution A for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages Figure 5. Node voltages of solution A for all tap position constraints (g –g ). (a) Node voltages A C (equality constraint of (equality constraint of𝑔 g ); ( ); b (b ) n ) node ode voltag voltages es (e(equality quality constra constraint int of of 𝑔 g ); ( ); c (c ) nod ) node e vo voltages ltages (equ (equality ality A B constraint of constraint of𝑔 g ). ). Simulation findings manifest a decisive improvement of voltage profile with stability indicator. Comparison of Figure 4b, c shows an entire system of stable operation and voltage profile transition from lower than 0.85 pu to more than 0.98 pu. Previous studies relied on optimal placement of control devices; while, this study in addition to optimal placement of control devices (Tables 3–6), focused on optimum number of control devices to ensure technical and economic dimensions within a single solution. Figure 4d shows the Pareto optimum solution, which indicates the relationship between the number of ATCTrs and voltage deviation. Besides, number of tap position changes have also been considered as an important factor in a rough equipment life time of an ATCTr depreciation. Increasing changing tap position can significantly reduce a contact lifespan, and accelerates deterioration of (a) (b) (c) Appl. Sci. 2019, 9, x 8 of 12 Table 6. Optimum placement of ATCTrs (solution D). Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Equality 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 constraint of gA Equality 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 constraint of gB Equality 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 constraint of gC The voltage waveforms for solutions A–D has shown in Figures 5–8. (a) (b) Appl. Sci. 2019, 9, 2813 9 of 13 (c) transformer oil in switching process. Therefore, the control of changing tap potions is a known exigence. Figure 5. Node voltages of solution A for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages As shown in Figures 5–8, depending on the equality constraints (g –g ), reducing voltage deviation A C (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality for constants (g and g ) is very close (almost equal). With automatic control and using g instead of B C B constraint of 𝑔 ). g (in addition to setting voltage) enhances the lifespan of ATCTr. (a) (b) Appl. Sci. 2019, 9, x 9 of 12 (c) Figure 6. Node voltages of solution B for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages Figure 6. Node voltages of solution B for all tap position constraints (g –g ). (a) Node voltages A C (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality (equality constraint of g ); (b) node voltages (equality constraint of g ); (c) node voltages (equality A B constraint of 𝑔 ). constraint of g ). (b) (a) (c) Figure 7. Node voltages of solution C for all tap position constraints (g –g ). (a) Node voltages A C Figure 7. Node voltages of solution C for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages (equality constraint of g ); (b) node voltages (equality constraint of g ); (c) node voltages (equality A B (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality constraint of g ). constraint of 𝑔 ). (b) (a) (c) Figure 8. Node voltages of solution D for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality constraint of 𝑔 ). Simulation findings manifest a decisive improvement of voltage profile with stability indicator. Comparison of Figure 4b, c shows an entire system of stable operation and voltage profile transition from lower than 0.85 pu to more than 0.98 pu. Previous studies relied on optimal placement of control Appl. Sci. 2019, 9, x 9 of 12 Figure 6. Node voltages of solution B for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality constraint of 𝑔 ). (b) (a) (c) Figure 7. Node voltages of solution C for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality Appl. Sci. 2019, 9, 2813 10 of 13 constraint of 𝑔 ). (b) (a) (c) Figure 8. Node voltages of solution D for all tap position constraints (g –g ). (a) Node voltages A C Figure 8. Node voltages of solution D for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages (equality constraint of g ); (b) node voltages (equality constraint of g ); (c) node voltages (equality A B (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality constraint of g ). constraint of 𝑔 ). The results are visualized in Figures 5–8, and simulation findings are summarized in Table 7. Simulation findings manifest a decisive improvement of voltage profile with stability indicator. Comparison of Figure 4b, c shows an entire system of stable operation and voltage profile transition Table 7. Comprehensive results of Figures 5–8. from lower than 0.85 pu to more than 0.98 pu. Previous studies relied on optimal placement of control Solution A Solution B Solution C Solution D Voltage magnitude for g 0.8995 0.9397 0.9413 0.9487 Voltage magnitude for g 0.9157 0.9457 0.9642 0.9711 Voltage magnitude for g 0.9251 0.9641 0.9703 0.9786 The first column of Table 7 shows voltage magnitudes for solution A, which shows an increase in accordance with equality constraints (g –g ), respectively. In the second column, by adding the A C number of ATCTrs in solution B, voltage magnitudes are maintained at statutory limits (0.95  V  1.05 pu). For constant g , voltage is at an acceptable range. In the third column of Table 7, in addition to maintaining voltage in an appropriate range, a comparison of g and g indicate that voltage values B C are very close and almost equal (Figure 7). Furthermore, the fourth column shows the similarity of the voltage magnitudes for constants g and g as well (Figure 8). Hence, using g is preferred compared B C B to g for ATCTr ’s better performances. For the entire system, the proposed method can improve reinstates busses voltage to rated level and maintain unity behavior among all buses in term of voltage profile. Results indicate that in the presence of the ATCTrs, voltage stability and profile for entire distribution system can be improved. Meanwhile, it can maintain voltage at a proper and statutory range by installing ATCTrs in less than half nodes. 5. Conclusions This paper evaluates the e ectiveness of ATCTr as a voltage control device with respect to voltage deviation. This study o ers a viable solution for reliable operation of a distribution system in term of voltage deviation control and power transfer improvement. Di erent from the literature that propose optimal placement of (ATCTr) in a system, this study considers the optimum required number of ATCTr as well. The results indicate the e ectiveness of the proposed solution from technical and Appl. Sci. 2019, 9, 2813 11 of 13 economic standpoints. The multiobjective optimization using genetic algorithm (GA) was used based on Newton–Raphson power flow with the objectives of minimizing voltage deviation and simultaneously minimizing the number of introduced voltage control devices. The 22-bus real distribution network was simulated. The proposed algorithm (GA) was compared di erent cases with specifying the optimum number of ATCTr using Pareto front method. From the findings, this method can e ectively overcome the voltage regulation problem by giving optimum location and required number of (ATCTrs). Author Contributions: Conceptualization, S.M.S.D.; methodology, S.M.S.D. and R.S.; resources, P.M., N.K. and A.M.H.; data curation, M.K.; writing—original draft preparation, S.M.S.D. and M.K.; writing—review and editing, S.M.S.D.; supervision, T.S. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflicts of interest. Abbreviations a The number of introduced ATCTr at each node i F , F Objective functions 1 2 g  g The numbers of tap change position A C N Total number of buses P The placement of installed ATCTrs in overall nodes T The tap position of node i T , T Lower and upper tap position limits min max V Distribution voltage of node i V Voltage deviation on each node i at time t i,t V , V . Voltage’s lower and upper limits respectively min max x The number of taps changing positiont time t References 1. Furukakoi, M.; Adewuyi, O.B.; Danish, M.S.S.; Howlader, A.M.; Senjyu, T.; Funabashi, T. Critical Boundary Index (CBI) based on active and reactive power deviations. Int. J. Electr. Power Energy Syst. 2018, 100, 50–57. [CrossRef] 2. United States Agency for International Development (USAID) South Asia Regional Initiative for Energy (SARI/EI). Available online: https://sari-energy.org/oldsite/PageFiles/Countries/Afghanistan_Energy_detail. html (accessed on 25 March 2019). 3. Shigenobu, R.; Yona, A.; Senjyu, T. Multi-objective optimization of ATCTr considering optimum placement and weather conditions in distribution systems. In Proceedings of the 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia), Seoul, Korea, 1–5 June 2015; pp. 2235–2240. 4. Liu, X.; Aichhorn, A.; Liu, L.; Li, H. 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Analysis of tap-changer dynamics and construction of voltage stability regions. IEEE Trans. Circuits Syst. 1989, 36, 575–590. [CrossRef] 9. Mendes, A.; Boland, N.; Guiney, P.; Riveros, C. Switch and Tap-Changer Reconfiguration of Distribution Networks Using Evolutionary Algorithms. IEEE Trans. Power Syst. 2013, 28, 85–92. [CrossRef] Appl. Sci. 2019, 9, 2813 12 of 13 10. Osmanbasic, E.; Skelo, G. Tap Changer Condition Assessment Using Dynamic Resistance Measurement. Procedia Eng. 2017, 202, 52–64. [CrossRef] 11. Erbrink, J.J.; Gulski, E.; Seitz, P.P.; Leich, R. Advanced on-site diagnosis of transformer on-load tap changer. In Proceedings of the Conference Record of the 2008 IEEE International Symposium on Electrical Insulation, Vancouver, BC, Canada, 9–12 June 2008; pp. 252–256. 12. Hussain, M.M.; Zakaria, Z.; Rizman, Z.I.; Yasin, M.A.M. Power loss estimation due to di erence transformer tap changer position at interface. J. Fundam. Appl. Sci. 2017, 9, 685–696. [CrossRef] 13. Becirovic, V.; Hasanic, M.; Dozic, N.; Hanjalic, S.; Curevac, S.; Nikolic, B. Optimal control of small hydropower plants and power transformer tap changer in distribution network in order to minimize active power losses. In Proceedings of the 2015 5th International Youth Conference on Energy (IYCE), Pisa, Italy, 27–30 May 2015; pp. 1–8. 14. Jaramillo-Duque, Á.; Muñoz-Galeano, N.; Ortiz-Castrillón, J.R.; López-Lezama, J.M.; Albarracín-Sánchez, R. Power Loss Minimization for Transformers Connected in Parallel with Taps Based on Power Chargeability Balance. Energies 2018, 11, 439. [CrossRef] 15. Kang, P.; Birtwhistle, D. Condition Assessment of Power Transformer on-Load Tap-Changers Using Wavelet Analysis and Self-Organizing Map: Field Evaluation. IEEE Power Eng. Rev. 2002, 22, 69. [CrossRef] 16. Danish, M.S.S.; Senjyu, T.; Danish, S.M.S.; Sabory, N.R.; Mandal, P. A Recap of Voltage Stability Indices in the Past Three Decades. Energies 2019, 12, 1544. [CrossRef] 17. 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In Proceedings of the 2012 International Conference on Power, Signals, Controls and Computation, Thrissur, Kerala, India, 3–6 January 2012; pp. 1–6. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Sciences Multidisciplinary Digital Publishing Institute

A Real Distribution Network Voltage Regulation Incorporating Auto-Tap-Changer Pole Transformer Multiobjective Optimization

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applied sciences Article A Real Distribution Network Voltage Regulation Incorporating Auto-Tap-Changer Pole Transformer Multiobjective Optimization 1 , 2 1 3 Sayed Mir Shah Danish * , Ryuto Shigenobu , Mitsunaga Kinjo , Paras Mandal , 4 5 1 Narayanan Krishna , Ashraf Mohamed Hemeida and Tomonobu Senjyu Faculty of Engineering, University of the Ryukyus, 1 Senbaru Nishihara-cho, Nakagami, Okinawa 903-0213, Japan Department of Electrical and Electronics Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui-shi, Fukui 910-8507, Japan Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX 79968, USA Department of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur, Tamil Nadu 613401, India Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt * Correspondence: sayedmir.danish@gmail.com Received: 11 June 2019; Accepted: 11 July 2019; Published: 14 July 2019 Abstract: A number of studies realized operation of power systems are unstable in developing countries due to misconfiguration of distribution systems, limited power transfer capability, inconsistency of renewable resources integration, paucity of control and protection measures, timeworn technologies, and disproportionately topology. This study underlines an Afghanistan case study with 40% power losses that is mainly pertinent from old distribution systems. The long length of distribution systems, low-power transfer capability, insucient control and protection strategy, peak-demand elimination, and unstable operation (low energy quality and excessive voltage deviations) are perceived pre-eminent challenges of Afghanistan distribution systems. Some attainable solutions that fit challenges are remodeling (network reduction), networks reinforcement, optimum compensation strategy, reconfiguration options, improving, and transfer capability. This paper attempts to propose a viable solution using multiobjective optimization method of auto-tap-changer pole transformer (ATCTr). The proposed methodology in terms of optimal numbers and placement of ATCTr can be known as a novel two-dimensional solution. For this purpose, a real case of Kabul City distribution system is evaluated. Simulation results indicate the e ectiveness of the proposed method in reducing system losses and improving system overall performance. This approach tends to regulate the voltage deviation in a proper and statutory range with minimum number and optimum placement of ATCTrs. The proposed method is simulated using MATLAB environment to compare and evaluate performance of the proposed network under di erent situations and scenarios. Keywords: auto-tap-changer pole transformer (ATCTr); distribution network; genetic algorithm (GA); multiobjective optimization; voltage deviation control; voltage regulation; voltage stability 1. Introduction Electric power distribution system with multifarious topologies, configurations, and characteristics is one of the salient components of a power system. In most developing countries, increasing demand for electrical energy enforces distribution systems for an increasingly expansion and broadening. For any expansion, power energy quality and eciency require special attention of control, improvement, Appl. Sci. 2019, 9, 2813; doi:10.3390/app9142813 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 2813 2 of 13 and management. One of the most e ective factors in an electric power energy quality is voltage deviation and stability. Extension of a network length and expansion of topology can be associated with the risk of statutory and standard limit [1]. Kabul is a densely populated and capital city of Afghanistan that distribution networks su er unstable-rated operation. These networks are extended without length limitation consideration, which demonstrates unstable voltage beyond the statutory range with huge technical and economic losses. In recent years, the government of Afghanistan bounded to retain environmental protection and sustainable development in accordance with the Paris Agreement 2015 (combat climate change), and Sustainable Development Goals (SDGs) 2030. Reform of the energy sector has been part of this endeavor. Afghanistan’s distribution networks are the least developed and old-fashioned part of the power system. In addition to the technical and financial losses, shortage of access to electric energy has led to increased utilization of primary energy resources and fossil fuel with high environmental impact. Meanwhile, distribution systems to remote areas are extended without expandability capacity (in local and regional networks) consideration. In priority, it must seriously consider and adopt appropriate solutions. The e ective delivery of power to the end users can be achieved by improving reliability, eciency, cost-e ectiveness, and sustainability measures of production and distribution [2]. Various investigations using di erent optimization techniques, methods, and solutions are conducted to regulate voltage and reduce energy losses in a distribution system. In [3], multiobjective optimization of auto-tap-changer pole transformer with respect to minimizing the voltage deviation of a 16-bus distribution network was tested. In [4], a coordinated control of distributed energy storage system (ESS) with traditional voltage regulators including on-load tap changer transformers (OLTC) and step voltage regulators (SVR) was applied. Authors of [5] proposed data fusion theory to develop a comparative diagnostic method to determine the operation status of on-load tap changers mechanism. A study was carried out in [6] to enhance power quality with automatic tap change in transformer in a smart grid distribution system. In [7], an implementation of a prototype electronic tap-changer instead of mechanical tap-changer was proposed. This method was demonstrated with some shortcomings, such as low operating speed, short lifetime, and heavy size. In [8], the authors employed a nonlinear dynamic model of OLTC, impedance loads, and decoupled reactive power voltage relations to reconstruct the voltage collapse phenomenon. This method aims to determine operation status of on-load tap changers mechanism. Likewise, in [9], a network reconfiguration was carried out over two domains simultaneously: Re-switching strategies and transformer tap-changer adjustments. Similarly, several techniques and strategies for voltage stability enhancement and regulation have been applied, using several case studies under di erent conditions [10–15]. This study aims to present a fully solid-state tap-changer solution with a new control strategy and optimal configuration. Over the past decades, power system blackouts due to voltage instability were repeatedly reported; namely, Tokyo blackout on 23 July 1987 and United Kingdom, Sweden, Canada, Denmark, Italy, and the United States blackouts in 2003 [16]. Power system voltage stability has been discussed enough over the past decades. In [17], a control strategy for reactive power compensation using storage system was studied. This study aimed to improve system stability by a proper prediction of reactive behavior and demand for di erent operation conditions. In [18], the authors presented the stability analysis using the load and generation levels as a direction vector for the base system through continuation power flow (CPF) under normal condition and contingency. The authors of [19] proposed a wavelet transform (WT) based on data analysis to extract the features from real-time active power and RMS (root mean square) voltage of the power grid. This study applied a hybrid classification technique based on particle swarm optimization (PSO) and support vector machines (SVM) to classify the features and diagnose di erent types of faults in a smart grid system. Previous studies investigated the use of control devices in a variety of ways based on di erent optimization methods. Most of these studies were focused on required number of control device without considering the optimum placement and number of these devices. The proposed methods can technically be feasible, but economically they are not acceptable. Therefore, reducing number of control Appl. Sci. 2019, 9, x 3 of 12 Previous studies investigated the use of control devices in a variety of ways based on different optimization methods. Most of these studies were focused on required number of control device without considering the optimum placement and number of these devices. The proposed methods Appl. Sci. 2019, 9, 2813 3 of 13 can technically be feasible, but economically they are not acceptable. Therefore, reducing number of control devices in a system is another important objective. To solve the trade-off problem, the devices in a system is another important objective. To solve the trade-o problem, the multiobjective multiobjective optimization is an excellent tool. Additionally, the increase in tap position changing optimization is an excellent tool. Additionally, the increase in tap position changing can reduce contacts can reduce contacts lifespan and accelerate deterioration of transformer oil in the switching process lifespan and accelerate deterioration of transformer oil in the switching process [20]. This paper [20]. This paper provides a method of multiobjective optimization of auto-tap-changer pole provides a method of multiobjective optimization of auto-tap-changer pole transformer (ATCTr), in term transformer (ATCTr), in term of optimum number and placement of tap position changes. of optimum number and placement of tap position changes. Meanwhile, a multiobjective optimization Meanwhile, a multiobjective optimization using genetic algorithm [21–25] is applied to minimize using genetic algorithm [21–25] is applied to minimize voltage deviation. In Section 2, characteristics voltage deviation. In Section 2, characteristics of system model and problem description are of system model and problem description are discussed. Section 3 presents the methodology, follows discussed. Section 3 presents the methodology, follows by the simulation result and comparison in by the simulation result and comparison in Section 4. At last, Section 5 concludes simulations findings Section 4. At last, Section 5 concludes simulations findings and briefs novelty and effectiveness of the and briefs novelty and e ectiveness of the study. study. 2. Characteristics of the System Model and Problem Description 2. Characteristics of the System Model and Problem Description The targeted model in this study was located in Kabul city (capital of Afghanistan). Triple energy The targeted model in this study was located in Kabul city (capital of Afghanistan). Triple energy sectors, generation, transmission, and distribution systems, su er technical and economic losses. sectors, generation, transmission, and distribution systems, suffer technical and economic losses. After a long-term political instability and lack of maintenances, Kabul city distribution networks After a long-term political instability and lack of maintenances, Kabul city distribution networks demonstrate many problems; namely, transformer no-load loss, imbalance between primary and demonstrate many problems; namely, transformer no-load loss, imbalance between primary and secondary distribution systems in terms of power transfer, scattered distribution transformer from secondary distribution systems in terms of power transfer, scattered distribution transformer from gravity center of load, unbalance reactive power and distributed three phase supply, lack of protection gravity center of load, unbalance reactive power and distributed three phase supply, lack of devices, long length of customers cables, use of nonstandard equipment, etc. [26]. Reports pertain protection devices, long length of customers cables, use of nonstandard equipment, etc. [26]. Reports 25–40% losses to distribution systems that require a viable solution and proper management of technical pertain 25–40% losses to distribution systems that require a viable solution and proper management and economic losses [27]. Meanwhile, an increasing population growth forces distribution networks to of technical and economic losses [27]. Meanwhile, an increasing population growth forces operate close to their stability limit within maximum expandability [28]. Definitely, system expansion distribution networks to operate close to their stability limit within maximum expandability [28]. under stressed voltage control condition has a direct impact on voltage profile and power losses [29]. Definitely, system expansion under stressed voltage control condition has a direct impact on voltage For this case study, voltage deviation at distribution level is out of acceptable range; whereas, at the profile and power losses [29]. For this case study, voltage deviation at distribution level is out of time of peak load demand, it reaches 15% voltage deviation. acceptable range; whereas, at the time of peak load demand, it reaches 15% voltage deviation. Figure 1 shows the proposed 20 kV distribution system consisting of 22 buses and 21 lines that Figure 1 shows the proposed 20 kV distribution system consisting of 22 buses and 21 lines that are considered a real model of simulation. Table 1 illustrates the mentioned distribution system are considered a real model of simulation. Table 1 illustrates the mentioned distribution system transmission lines parameters. The proposed model supplies residential, commercial, and industrial transmission lines parameters. The proposed model supplies residential, commercial, and industrial consumers. This system consists of transformer stations (TSs) and junction station (JS-6) that feeds consumers. This system consists of transformer stations (TSs) and junction station (JS-6) that feeds from the (110/20 KV, 50 MVA Breshna Kot substation). from the (110/20 KV, 50 MVA Breshna Kot substation). Figure Figure 1. 1. Bresh Breshna na Kot distribu Kot distribution tion network network model. model. Table 1. Kabul city 20 kV distribution system transmission line parameters. Bus Code Line Number Length (km) R (pu) X (pu) From To 1 1 2 0.75 0.246 0.072375 2 2 3 0.8 0.2624 0.0772 Appl. Sci. 2019, 9, 2813 4 of 13 Table 1. Kabul city 20 kV distribution system transmission line parameters. Bus Code Length (km) R (pu) X (pu) Line Number From To 1 1 2 0.75 0.246 0.072375 2 2 3 0.8 0.2624 0.0772 3 3 4 0.6 0.1968 0.0579 4 3 12 0.4 0.1312 0.0386 5 4 5 0.65 0.2132 0.062725 6 5 6 0.95 0.3116 0.091675 7 5 13 0.7 0.2296 0.06755 8 6 7 0.65 0.2132 0.062725 9 6 14 1.4 0.4592 0.1351 10 14 15 0.6 0.1968 0.0579 11 7 8 0.8 0.2624 0.0772 12 7 16 0.65 0.2132 0.062725 13 16 17 0.6 0.1968 0.0579 14 17 18 0.55 0.1804 0.053075 15 8 9 0.65 0.2132 0.062725 16 9 10 0.4 0.1312 0.0386 17 9 19 0.8 0.2624 0.0772 18 19 20 0.45 0.1476 0.043425 19 20 21 0.4 0.1312 0.0386 20 21 22 0.4 0.1312 0.0386 21 10 11 0.45 0.1476 0.043425 3. Methodology Maintaining stable operation and reliable supply remain the first ever anticipation of any distribution system [30]. The e ectiveness of voltage control device over available approaches for voltage stability and control are highlighted in the literature. This study targets ATCTr from di erent standpoints of optimum selection, requirement, and placement. Proper planning of ATCTr contributes voltage stability and improve voltage profile with minimum number of control devices. Since ATCTr devices are expenses, considering the minimum penetration of these devices with optimum placement can optimize resources technically and economically (installation and maintenances costs). This paper deals with optimum required number and placement of ATCTr using multiobjective algorithm. 3.1. Multiobjective Optimization Using Genetic Algorithm Multiobjective formulations are realistic models for many complex engineering optimization problems. A reasonable solution to a multiobjective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. Multiobjective optimization using genetic algorithm (GA) is approached in this paper to obtain the optimal number of ATCTr, and minimize voltage deviation [21–25]. Load flow analysis is simulated by Newton–Raphson (NR) method [31]. The current distribution network is considered as a single-phase model, operating under a balanced state. 3.2. Objective Functions Selection of the objective functions is a significant task to obtain an optimum solution in an optimization problem. It also necessarily a ects optimization behavior as well. In this study, two objective functions are considered for optimization as shown in Equations (1) and (2). min : F = a (1) 1 i i=1 Appl. Sci. 2019, 9, 2813 5 of 13 min : F = (V 1) (2) 2 i,t i=1 where, F is the objective function, it represents the total number of installed ATCTr, and F is another 1 2 objective function represents overall voltage deviation of nodes. a represents the number of introduced ATCTr at each node i, V is the voltage deviation on each node i at time t, N is the total number i,t of nodes. Constraint inequalities are as follows: V  V  V (3) min i max T  T  T (4) min i max where, V is the distribution voltage of node i; V , V are voltage lower and upper limits, respectively. i min max T is the tap position of node i; T , T are the tap position lower limit and tap position upper max i min limit, respectively. Equality restriction is as follows: g : x = 5 (5) A t t=0 g : x = 10 (6) B t t=0 g : x = 15 (7) t=0 where g –g are the constraints of the number of tap change position, and x is the tap change position A C number at time t. 3.3. Optimal Placement Problem Optimal placement problem of control devices remains a serious issue. Sometimes, disarrangement of control device not only cannot be e ective, also can be associated with technical and economic losses. Likewise, if equipment is not fit in an optimal location, its e ectiveness decreases and is not technically feasible. When the objective function is set to minimize voltage deviation and number of installed ATCTr, in this scenario, voltage control eciency depends on the placement of ATCTr [32,33]. Moreover, optimal scheduling of devices depends on the placement of the devices. Therefore, optimum placement of ATCTrs can reduce voltage deviation. Multiobjective optimization using GA with the objective function of voltage deviation was applied to solve the optimization problem. The proposed method aims to hence perform a power flow analysis, to calculate voltage magnitudes at di erent buses. GA randomly in each process locates ATCTr in di erent nodes with di erent alignments and configurations. These processes are repeatedly carried out until the comparison between all genes is made. Finally, the best gene (optimal placement) with least voltage deviation is specified from comparing the last population with the best gene from the new population. In order to take into account, the optimal placement of ATCTrs, a string of N bits (representing the total N nodes) was used to decide the location nodes at which to introduce an ATCTr, as shown below: P = (a , a , : : : , a ), (a 2 1, 0) (8) N N1 1 i where, P represents the placement of installed ATCTrs in overall nodes. Here, “0” represents a node with no ATCTr, whereas “1” represents a node with ATCTr-installed bus. Figure 2 and Table 2 represent an example of coding used for multiobjective optimization; the placement of installed ATCTrs in distribution network is demarcated with circles. Appl. Sci. 2019, 9, 2813 6 of 13 Appl. Sci. 2019, 9, x 6 of 12 Appl. Sci. 2019, 9, x 6 of 12 Figure 2. Figure 2. An ex An example ample of auto-t of auto-tap-changer ap-changer pole transfor pole transformer mer ((A ATCTr) installation placement cording. TCTr) installation placement cording. Figure 2. An example of auto-tap-changer pole transformer (ATCTr) installation placement cording. Table 2. Binary coding use for ATCTrs placement in nodes. Table 2. Binary coding use for ATCTrs placement in nodes. Table 2. Binary coding use for ATCTrs placement in nodes. Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Installed position 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Installed position 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Installed position 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Comprehensively, the stages of the proposed methodology are demonstrated in the flowchart Comprehensively, the stages of the proposed methodology are demonstrated in the flowchart Comprehensively, the stages of the proposed methodology are demonstrated in the flowchart shown in Figure 3. shown in Figure 3. shown in Figure 3. Figure 3. Figure 3. Flow Flowchart chart of the of the multiobject multiobjective ive opt optimization imization of of A ATCT TCTrs. rs. Figure 3. Flowchart of the multiobjective optimization of ATCTrs. 4. Simulation Result and Comparison 4. Simulation Result and Comparison 4. Simulation Result and Comparison To confirm the e ectiveness of introducing ATCTr (with a provision of10% change in voltage at To confirm the effectiveness of introducing ATCTr (with a provision of ±10% change in voltage To confirm the effectiveness of introducing ATCTr (with a provision of ±10% change in voltage 1.25% additional voltage per tap), simulation results based on the physical structure of the current at 1.25% additional voltage per tap), simulation results based on the physical structure of the current at 1.25% additional voltage per tap), simulation results based on the physical structure of the current distribution system is shown in Figure 1. distribution system is shown in Figure 1. distribution system is shown in Figure 1. Proper range for voltage deviation as defined by standard is 0.95  V  1.05 pu. The proposed Proper range for voltage deviation as defined by standard is 0.95 ≤ 𝑉 ≤1.05 pu. The proposed Proper range for voltage deviation as defined by standard is 0.95 ≤ 𝑉 ≤1.05 pu. The proposed distribution network parameters considering daily load profile and real-time voltage profile of the distribution network parameters considering daily load profile and real-time voltage profile of the distribution network parameters considering daily load profile and real-time voltage profile of the entire system are plotted in Figure 4a,b, respectively. This is followed by the distribution voltage entire system are plotted in Figure 4a and 4b, respectively. This is followed by the distribution voltage entire system are plotted in Figure 4a and 4b, respectively. This is followed by the distribution voltage magnitude using ATCTrs in Figure 4c. The number of tap position changes in a 24-h period (g ) is magnitude using ATCTrs in Figure 4c. The number of tap position changes in a 24-h period (𝑔 ) is 15 magnitude using ATCTrs in Figure 4c. The number of tap position changes in a 24-h period (𝑔 ) is 15 15 times. Moreover, the Pareto optimum solution for minimizing the number of introduced ATCTrs times. Moreover, the Pareto optimum solution for minimizing the number of introduced ATCTrs and times. Moreover, the Pareto optimum solution for minimizing the number of introduced ATCTrs and and minimizing of the voltage deviation considering tap position changes is shown in Figure 4d. minimizing of the voltage deviation considering tap position changes is shown in Figure 4d. minimizing of the voltage deviation considering tap position changes is shown in Figure 4d. Appl. Sci. 2019, 9, 2813 7 of 13 Appl. Sci. 2019, 9, x 7 of 12 (a) (b) (c) (d) Figure 4. Real distribution network model: (a) Daily load profile; (b) hourly voltage profile (uncontrolled); Figure 4. Real distribution network model: (a) Daily load profile; (b) hourly voltage profile (c) voltage profile using ATCTrs (controlled); (d) Pareto optimal solutions. (uncontrolled); (c) voltage profile using ATCTrs (controlled); (d) Pareto optimal solutions. Genetic algorithm (GA) as a multiobjective optimization technique is used to obtain study Genetic algorithm (GA) as a multiobjective optimization technique is used to obtain study objectives. Since the number of tap position changes is representative of a rough equipment lifetime, objectives. Since the number of tap position changes is representative of a rough equipment lifetime, multiobjective optimization was solved for tap position using the number of tap position changes as multiobjective optimization was solved for tap position using the number of tap position changes as a parameter (g –g ). Tables 3–6 show the location of ATCTrs for the solutions A–D. Pareto optimal A C a parameter (𝑔 –𝑔 ). Tables 3–6 show the location of ATCTrs for the solutions A–D. Pareto optimal solutions are shown in Figure 4d. solutions are shown in Figure 4d. Table 3. Optimum placement of ATCTrs (solution A). Table 3. Optimum placement of ATCTrs (solution A). Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Equality constraint Equality 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 of g constraint of gA Equality Equality constraint 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 constraint of gB of g Equality Equality constraint0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 constraint of gC 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 of g Table 4. Optimum placement of ATCTrs (solution B). Table 4. Optimum placement of ATCTrs (solution B). Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Equality 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 constraint of gA Equality constraint 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 Equality of g 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 constraint of gB Equality constraint Equality 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 of g constraint of gC Equality constraint 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 of g Table 5. Optimum placement of ATCTrs (solution C). Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Equality 0 0 1 0 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 0 1 1 constraint of gA Equality 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 0 1 0 1 constraint of gB Equality 0 1 0 1 0 1 1 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 constraint of gC Appl. Sci. 2019, 9, 2813 8 of 13 Table 5. Optimum placement of ATCTrs (solution C). Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Equality constraint 0 0 1 0 1 1 0 1 1 0 1 0 1 0 1 1 0 1 1 0 1 1 of g Equality constraint 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 0 1 1 0 1 0 1 of g Equality constraint 0 1 0 1 0 1 1 1 0 1 0 1 1 1 0 0 1 0 1 0 1 1 of g Table 6. Optimum placement of ATCTrs (solution D). Appl. Sci. 2019, 9, x 8 of 12 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Table 6. Optimum placement of ATCTrs (solution D). Equality constraint 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 of g Equality Equality constraint1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 constraint of gA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 of g Equality 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Equality constraint constraint of gB 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 of g Equality 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 constraint of gC The voltage waveforms for solutions A–D has shown in Figures 5–8. The voltage waveforms for solutions A–D has shown in Figures 5–8. (a) (b) (c) Figure 5. Node voltages of solution A for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages Figure 5. Node voltages of solution A for all tap position constraints (g –g ). (a) Node voltages A C (equality constraint of (equality constraint of𝑔 g ); ( ); b (b ) n ) node ode voltag voltages es (e(equality quality constra constraint int of of 𝑔 g ); ( ); c (c ) nod ) node e vo voltages ltages (equ (equality ality A B constraint of constraint of𝑔 g ). ). Simulation findings manifest a decisive improvement of voltage profile with stability indicator. Comparison of Figure 4b, c shows an entire system of stable operation and voltage profile transition from lower than 0.85 pu to more than 0.98 pu. Previous studies relied on optimal placement of control devices; while, this study in addition to optimal placement of control devices (Tables 3–6), focused on optimum number of control devices to ensure technical and economic dimensions within a single solution. Figure 4d shows the Pareto optimum solution, which indicates the relationship between the number of ATCTrs and voltage deviation. Besides, number of tap position changes have also been considered as an important factor in a rough equipment life time of an ATCTr depreciation. Increasing changing tap position can significantly reduce a contact lifespan, and accelerates deterioration of (a) (b) (c) Appl. Sci. 2019, 9, x 8 of 12 Table 6. Optimum placement of ATCTrs (solution D). Node Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 Equality 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 constraint of gA Equality 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 constraint of gB Equality 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 constraint of gC The voltage waveforms for solutions A–D has shown in Figures 5–8. (a) (b) Appl. Sci. 2019, 9, 2813 9 of 13 (c) transformer oil in switching process. Therefore, the control of changing tap potions is a known exigence. Figure 5. Node voltages of solution A for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages As shown in Figures 5–8, depending on the equality constraints (g –g ), reducing voltage deviation A C (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality for constants (g and g ) is very close (almost equal). With automatic control and using g instead of B C B constraint of 𝑔 ). g (in addition to setting voltage) enhances the lifespan of ATCTr. (a) (b) Appl. Sci. 2019, 9, x 9 of 12 (c) Figure 6. Node voltages of solution B for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages Figure 6. Node voltages of solution B for all tap position constraints (g –g ). (a) Node voltages A C (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality (equality constraint of g ); (b) node voltages (equality constraint of g ); (c) node voltages (equality A B constraint of 𝑔 ). constraint of g ). (b) (a) (c) Figure 7. Node voltages of solution C for all tap position constraints (g –g ). (a) Node voltages A C Figure 7. Node voltages of solution C for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages (equality constraint of g ); (b) node voltages (equality constraint of g ); (c) node voltages (equality A B (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality constraint of g ). constraint of 𝑔 ). (b) (a) (c) Figure 8. Node voltages of solution D for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality constraint of 𝑔 ). Simulation findings manifest a decisive improvement of voltage profile with stability indicator. Comparison of Figure 4b, c shows an entire system of stable operation and voltage profile transition from lower than 0.85 pu to more than 0.98 pu. Previous studies relied on optimal placement of control Appl. Sci. 2019, 9, x 9 of 12 Figure 6. Node voltages of solution B for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality constraint of 𝑔 ). (b) (a) (c) Figure 7. Node voltages of solution C for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality Appl. Sci. 2019, 9, 2813 10 of 13 constraint of 𝑔 ). (b) (a) (c) Figure 8. Node voltages of solution D for all tap position constraints (g –g ). (a) Node voltages A C Figure 8. Node voltages of solution D for all tap position constraints (𝑔 –𝑔 ). (a) Node voltages (equality constraint of g ); (b) node voltages (equality constraint of g ); (c) node voltages (equality A B (equality constraint of 𝑔 ); (b) node voltages (equality constraint of 𝑔 ); (c) node voltages (equality constraint of g ). constraint of 𝑔 ). The results are visualized in Figures 5–8, and simulation findings are summarized in Table 7. Simulation findings manifest a decisive improvement of voltage profile with stability indicator. Comparison of Figure 4b, c shows an entire system of stable operation and voltage profile transition Table 7. Comprehensive results of Figures 5–8. from lower than 0.85 pu to more than 0.98 pu. Previous studies relied on optimal placement of control Solution A Solution B Solution C Solution D Voltage magnitude for g 0.8995 0.9397 0.9413 0.9487 Voltage magnitude for g 0.9157 0.9457 0.9642 0.9711 Voltage magnitude for g 0.9251 0.9641 0.9703 0.9786 The first column of Table 7 shows voltage magnitudes for solution A, which shows an increase in accordance with equality constraints (g –g ), respectively. In the second column, by adding the A C number of ATCTrs in solution B, voltage magnitudes are maintained at statutory limits (0.95  V  1.05 pu). For constant g , voltage is at an acceptable range. In the third column of Table 7, in addition to maintaining voltage in an appropriate range, a comparison of g and g indicate that voltage values B C are very close and almost equal (Figure 7). Furthermore, the fourth column shows the similarity of the voltage magnitudes for constants g and g as well (Figure 8). Hence, using g is preferred compared B C B to g for ATCTr ’s better performances. For the entire system, the proposed method can improve reinstates busses voltage to rated level and maintain unity behavior among all buses in term of voltage profile. Results indicate that in the presence of the ATCTrs, voltage stability and profile for entire distribution system can be improved. Meanwhile, it can maintain voltage at a proper and statutory range by installing ATCTrs in less than half nodes. 5. Conclusions This paper evaluates the e ectiveness of ATCTr as a voltage control device with respect to voltage deviation. This study o ers a viable solution for reliable operation of a distribution system in term of voltage deviation control and power transfer improvement. Di erent from the literature that propose optimal placement of (ATCTr) in a system, this study considers the optimum required number of ATCTr as well. The results indicate the e ectiveness of the proposed solution from technical and Appl. Sci. 2019, 9, 2813 11 of 13 economic standpoints. The multiobjective optimization using genetic algorithm (GA) was used based on Newton–Raphson power flow with the objectives of minimizing voltage deviation and simultaneously minimizing the number of introduced voltage control devices. The 22-bus real distribution network was simulated. The proposed algorithm (GA) was compared di erent cases with specifying the optimum number of ATCTr using Pareto front method. From the findings, this method can e ectively overcome the voltage regulation problem by giving optimum location and required number of (ATCTrs). Author Contributions: Conceptualization, S.M.S.D.; methodology, S.M.S.D. and R.S.; resources, P.M., N.K. and A.M.H.; data curation, M.K.; writing—original draft preparation, S.M.S.D. and M.K.; writing—review and editing, S.M.S.D.; supervision, T.S. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflicts of interest. Abbreviations a The number of introduced ATCTr at each node i F , F Objective functions 1 2 g  g The numbers of tap change position A C N Total number of buses P The placement of installed ATCTrs in overall nodes T The tap position of node i T , T Lower and upper tap position limits min max V Distribution voltage of node i V Voltage deviation on each node i at time t i,t V , V . 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In Proceedings of the 2012 International Conference on Power, Signals, Controls and Computation, Thrissur, Kerala, India, 3–6 January 2012; pp. 1–6. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Published: Jul 14, 2019

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