Optimal Distributed Generation and Capacitor Coordination for Power Loss Minimization Mohd Nabil Bin Muhtazaruddin, Nguyen Duc Tuyen and Goro Fujita Shibaura Institute of Technology Tokyo, Japan
[email protected],
[email protected],
[email protected] Abstract—This paper describes the effectiveness of solving the coordination of Distributed Generation (DG) and capacitor simultaneously to minimize power loss in the distribution system. Previously, the solution is executed separately which means the coordination of DG is done without the presence of capacitor, and vice versa. Hence, possibility to obtain better results will be limited. A hybrid optimization based on Artificial Bee Colony (ABC) and Artificial Immune System (AIS) will be used to determine the optimal DG and capacitor coordination simultaneously. The proposed method was tested on a 33-bus test system with several cases studies by using MATLAB programming. The simulation results show that the simultaneous approach provides better power loss reduction and voltage profile enhancement compared to a separate analysis. Index Terms—Distributed power generation, power loss reduction, optimal capacitor coordination, optimal distributed generation coordination, voltage profile improvement.
I.
INTRODUCTION
Power loss is one of the most important aspects that need to be considered by utilities during the planning stage in order to design the most efficient distribution possible, especially during current times and the foreseeable future, where the prices of oil and gas continue to fluctuate. There are several existing approaches in the literature that deal with the reduction of power losses such as determining optimal Distributed Generation (DG) coordination (location and size), capacitor placement and network reconfiguration. Traditionally, all these approaches are carried out separately without considering each other in the analysis. Therefore, better quality of solution is not achievable due to the restrictions implied by the separation of optimization process. The introduction of DG into the distribution network has helped in improving the efficiency of the system. However, one of the issues needs to be solved by the utilities is the determination of the optimal coordination of the DG. Moreover, wrongly calculating the size and location of the DG can worsen the system performance [1].There are several techniques that have been proposed by the researchers to tackle this problem. Basically the solution can be categorized
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Jasrul Jamani Bin Jamian Universiti Teknologi Malaysia Johor Bahru, Malaysia
[email protected]
into two main groups, which are separate analysis and simultaneous analysis. In the separate analysis, calculation of the DG location is determined by using sensitivity factor [2],[3]; whereas the size of the DG is calculated by using optimization technique. On the other hand, in the simultaneous analysis, the location and the size of DG are determined at the same time by using optimization techniques such as Particle Swarm Optimization (PSO) [4], Genetic Algorithm (GA) [5] and Artificial Bee Colony (ABC) [6]. The capacitor coordination is another approach to minimize the power loss in the distribution network. Same as in DG coordination, it can be categorized into the two groups. Typically, capacitor placement is determined by using sensitivity index [7-9] that chooses the node which provides higher reduction of power losses when reactive power is supplied. For simultaneous approach, many authors use the optimization approach [10-12] to calculate the optimal value of capacitor size and location. Recently, several authors have proposed to harmonize these two methods in a single solution. Kalantari et al. [13] applied GA to optimize a DG and four capacitors simultaneously in the 28-bus test system. In addition, they also studied the placement effect of the DG and the capacitor at the same and different bus. Abu-Mouti et al. [6] suggested ABC for the simultaneous approach. In their analysis, they consider power factor for the DG as one of their constraints. Besides that, Elmitwally et al. [14] proposed a modified Simulated Annealing (SA) to solve the capacitor coordination to enhance quality of power by taking into account the total harmonic distortion. Aman et al. [15] applied PSO to solve simultaneous problem with objective is to reduce total power losses. In this paper, a method to obtain the optimal DG and capacitor coordination is proposed to reduce the total real power loss and to improve the voltage profile. The proposed method is executed by using the proposed hybrid optimization technique and was tested on the 33-bus system. The rest of this paper is organized as follows: Section II describes the problem formulation of the proposed method. Section III briefly discusses about the hybrid optimization
method which is a combination between Artificial Bee Colony (ABC) and Artificial Immune System (AIS). Section IV presents the simulation results and discussions in terms of power losses reduction and voltage profile performance. Section V concludes the paper. II.
PROBLEM FORMULATION
The main objective of this paper is to reduce the power losses in the distribution network by simultaneously determining the optimal DG output power, capacitor size, DG location and capacitor location. Therefore, the objective function can be expressed as shown in (1).
min f ( x1 , x 2 , x 3 , x 4 ) = where, x1 x2 x3 x4 nbl Ploss,i
nbl
∑ Ploss ,i i =1
(1)
DG output power (Continuous variable), DG location (Discrete variable), Capacitor size (Continuous variable), Capacitor location (Discrete variable), number of lines, Real power losses at line, i of the distribution system.
DG output constraint (continuous variable):
OPDG ,min ≤ OPDG ≤ OPDG ,max
e)
Power balance constraint totaldg
totalload
nbl
k =1
p =1
i =1
∑ PDG, k + Pmaingrid = ∑ Pload , p + ∑ Ploss,i
(6)
where Pmaingrid is a total power from the upper grid. III.
Several constraints are considered in the analysis to ensure that all important parameters are within the allowable limit. The list of constraints is shown below: a)
where totaldg and totalload are total number of DG and total number of load, respectively. While, OPDG is the output power of DG and Pload is the total load at bus p. This constraint is used to avoid the reverse power flow from occurring in the system.
HYBRID OPTIMIZATION
In this paper, a hybrid optimization which is known as Artificial Immune Bee Colony (AIBC) will be used to solve the DG and capacitor coordination simultaneously. This method was proposed by Muhtazaruddin et al. [16] to determine the optimal DG coordination with network reconfiguration simultaneously that involved high dimensional problem. The ABC optimization is proposed by Karaboga [17] and the basic concept of this algorithm duplicates the behavior of the bees finding the food around their hive. This process is solely done by three different types of bees which are employed, onlooker and scout as shown in Fig. 1. All of these bees play a different role to complete a given task.
(2)
where OPDG,max and OPDG,min are the maximum and minimum acceptable DG output, respectively. b) Capacitor size constraint (continuous variable):
C min ≤ C ≤ C max
(3)
where Cmax and Cmin are the maximum and minimum acceptable capacitor size, respectively. c)
Fig. 1. Flow chart of ABC
Voltage constraint
V p ,min ≤ V p ≤ V p ,max
(4)
where Vp,max and Vp,min are maximum and minimum acceptable voltage at bus p, respectively. d) Total output power of DG constraint totaldg
totalload
k =1
p =1
∑ OPDG , k < ∑ Pload , p
Basically, the ABC algorithm starts off by sending the scout bees out to randomly search for food. Based on their finding, they evaluate the richness of food (fitness) by using (7). During this phase, the scout bees will change its status as employed bees and share the information with onlooker bees at the dance area.
Fi = (5)
1 1 + OF i
(7)
where Fi is a fitness value at location i and OFi is an objective function as expressed in (1).
After that, they return back at previous food area, but choose another food source (variable) as in (8) and again evaluate the new fitness.
X ij
new
= X ij
old
+ Rand × ( X ij
old
− X kj )
(8)
where Xijnew and Xijold are the new variable and the previous variable, respectively. Rand is a randomly generated number between -1 and 1, whereas Xkj is neighbour value of variable that is selected randomly. Based from the old and new values of fitness, the bees will select only high values using the greedy selection process and share this information with onlooker bees at dance area. At this phase, onlooker bees will select the food sources based on the probability value as given in (9). However, if the fitness value didn’t improve for a specified number of times (until it reaches a certain predetermined limit), they will abandon the food source and assign a scout bee to explore new food source location randomly.
Pi =
Fi tfs
∑ Fj
(9)
j =1
where Pi is a probability at solution i and tfs is total number of food sources. For AIS algorithm, this optimization imitates the behavior of the immune system (human particularly), a natural defense mechanism to protect the human body from foreign substances [18]. Fig. 2 shows the general process of AIS in searching optimal solution. Based from that figure, it can be observed that the AIS method involves several processes which are the initialization process, the duplication process, the mutation process, the sorting process and the selection process. Therefore, by combining the AIS process (duplicate, sort and selection) on the ABC, it can help the algorithm to search all possible solution by giving the opportunity to all variables to update (mutation) at the same time [16]. Fig. 3 shows a flow chart of AIBC for solving DG and capacitor coordination simultaneously.
Fig. 3. Flow chart of AIBC
IV.
RESULTS AND DISCUSSIONS
The proposed method is validated on the 33-bus system as shown in Fig. 4, which consists of 33 buses and 32 lines. The system is connected to the 132/12.66 kV substation with the total of load at 3.715 MW and 2.300 MVar. All the data of the system can be obtained as in [19].
132/12.66 kV
22 23 24 25
2
18
2
23 24
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Fig. 2. Flowchart of AIS
1 3
6 7 8 9 10 11 12
20
20 21
4 5
19
19
3
21 22
25 26
26 27 28 29 30 31 32
27 28 29 30 31 32 33
13 14 15 16 17
Fig. 4. Original 33-bus test system
Four case studies are considered in this paper as presented in Table I. Case 1 is set as a reference case which doesn’t include any coordination methods. In the case 2, single capacitor is installed and calculates the optimal location and size simultaneously. On the other hand, for the case 3, single DG is installed and DG coordination technique is applied to determine optimal location and output power. For the last case which is the case 4, the capacitor and the DG coordination are determined simultaneously. TABLE I.
DESCRIPTION FOR VARIOUS CASE STUDIES
Further analysis is performed to test the convergence characteristic and consistency of the proposed method (case 4), as shown in Fig.6 and Table III, respectively. The simulation has been run separately for 10 times with 150 cycles each. Based from Fig.6, it can be observed that solution for case 4 converge between 8 to 35 cycles. Furthermore, performance in solving case 4 is consistent. This can be proved by referring to the Table III in which all the values of best, mean and worst is almost similar and for the standard deviation value only 0.0015 kW. 90
1 2 3 4
Table II shows the results comparison for all cases. Initial power loss for the 33-bus test system is 203.19 kW. From the obtained results, it can be clearly seen that the simultaneous integration of DG and capacitor can give superior power loss reduction at about 74.42% compared to the base case. In case 2, the total power loss is only reduced by 29.22% when the capacitor is installed. However, the power losses can be reduced further with the installation of DG as shown in case 3. TABLE II. Case 1 2 3 4
80
85
75
80 70
Total Power Losses (kW)
Description 33-bus test system without DG and capacitor (Base case) Optimal size and location of single capacitor is determined simultaneously. Optimal output power and location of single DG is determined simultaneously. Optimal coordination of the DG and the capacitor are determined simultaneously.
75
60
70 55
65
50 1
Capacitor size (MVar) and location 1.26 (bus 30) 1.25 (bus 30)
1
2
1 0.99 0.98 0.97 0.96 0.95 0.94 0.93 0.92 0.91 0.9
32 31 30 29 28 27 26
Total power losses (kW) 203.19 143.82 104.38 51.97
4 6 7 8
Cycles
TABLE III.
22
13 21
14 20
Case 1
19
18
Case 2
17
16
15
Case 3
Case 4
Fig. 5. Voltage profile for all cases
51.976 kW 51.973 kW 51.972 kW 0.0015 kW
Table IV shows several comparisons with other references in order to validate the effectiveness of the proposed methods. Based on the obtained results, the AIBC outperformed other methods in terms of reduction in total power loss. TABLE IV.
AIBC PSO [15]
COMPARISON WITH OTHER METHODS DG output (MW) and location 2.51 (bus 6) 2.51 (bus 6)
GA [20]
0.25 (bus 16) 0.25 (bus 22) 0.50 (bus 30)
Sensitivity Analysis (Location) and Heuristic Curve Fitting (Size) [21]
1.00 (bus 18)
11 12
PERFORMANCE FOR CASE 4
Best Mean Worst Standard Deviation
9 10
23
9 11 13 15 17 19 21 23 25 27 29 31 33 35
50
Method
5
24
7
55
3
25
5
Fig. 6. Convergence characteristic for case 4
Fig. 5 illustrates the voltage profile for all cases. It can be observed that the cases 2, 3 and 4 show better voltage profiles compared to the reference case. However, a significant improvement in voltage profile can be achieved when coordination of the capacitor and the DG are performed simultaneously as shown in case 4. 33
3
60
RESULTS FOR ALL CASES
DG output (MW) and location 2.58 (bus 6) 2.51 (bus 6)
1st run 2nd run 3rd run 4th run 5th run 6th run 7th run 8th run 9th run 10th run
65
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111 116 121 126 131 136 141 146
Case
Capacitor size (MVar) and location
Total power losses (kW)
1.25 (bus 30) 1.46 (bus 30) 0.30 (bus 15) 0.30 (bus 18) 0.30 (bus 29) 0.60 (bus 30) 0.30 (bus 31)
51.97 53.30*
1.00 (bus 33)
100.18*
71.56*
*The results had been simulated again due to different approach of power flow analysis
V.
CONCLUSION
In this paper, +which optimized coordination for the DG and the capacitor simultaneously is presented. The proposed method is executed using hybridization of Artificial Bee Colony and Artificial Immune System optimization methods. Several case studies have been conducted to test the effectiveness of the simultaneous approach over consecutive approach. The results showed that harmonizing of the DG coordination and the capacitor coordination into a single solution can provide better solution compared to separate or consecutive analysis.
[17] [18] [19]
[20]
References [1] [2]
[3] [4]
[5] [6]
[7]
[8] [9]
[10] [11] [12]
[13]
[14]
[15]
[16]
T. Griffin, K. Tomsovic, D. Secrest and A. Law, "Placement of dispersed generations systems for reduced losses," in Proc. 2000 IEEE Hawaii International Conference on System Sciences., pp. 1-9. Z. M. Yasin, T. K. A. Rahman, I. Musirin, and S. R. A. Rahim, "Optimal sizing of distributed generation by using Quantum-Inspired Evolutionary Programming," in Proc. 2010 IEEE Power Engineering and Optimization Conf., pp. 468-473. S. G. B. Dasan, S. S. Ramalakshmi and R. P. K. Devi, "Optimal siting and sizing of hybrid distributed generation using EP," in Proc. 2009 International conference on Power Systems, pp. 1-6. S. M. H. Nabavi, S. Hajforoosh, and M. A. S. Masoum, "Placement and sizing of distributed generation units for congestion management and improvement of voltage profile using Particle Swarm Optimization," in Proc. 2011 IEEE Innovative Smart Grid Technologies, pp. 1-6. D. Singh and K. S. Verma, "Multiobjective optimization for DG planning with load models," IEEE Trans. Power Systems, vol. 24, pp. 427-436, Feb. 2009. F. S. Abu-Mouti and M. E. EL-Hawary, "Optimal distributed generation allocation and sizing in distribution system via Artificial Bee Colony," IEEE Trans. Power Delivery, vol. 26, pp. 2090-2101, Oct. 2011. R. S. Rao, S. V. L. Narashimham and M. Ramalingaraju, "Optimal capacitor placement in a radial distribution system using Plant Growth Simulation algorithm," International Journal of Electrical Power and Energy Systems, vol. 33, pp. 1133-1139, Jun. 2011. K. Parakash and M. Sydulu, "Particle Swarm Optimization based capacitor placement on radial distribution systems," in Proc. 2007 IEEE Power Engineering Society General Meeting Conf., pp. 1-5. S. P. Singh and A. R. Rao., "Optimal allocation of capacitors in distribution systems using Particle Swarm Optimization," International Journal of Electrical Power and Energy Systems, vol. 43, pp. 12671275, Dec. 2012. A. H. H. Al-Mohammed and I. Elamin,"Capacitor placement in distribution systems using artificial intelligent techniques," in Proc. 2003 Power Tech. Conf., pp. 7-13. K. S. Swarup, "Genetic Algorithm for optimal capacitor allocation in radial distribution systems," in Proc. 2005 Evolution Computing Conf., pp. 152-159. M. A. S. Masoum, M. Ladjevardi, A. Jafarian and E. F. Fuchs, "Optimal placement, replacement and sizing of capacitor banks in distorted distribution network by Genetic Algorithms," IEEE Trans. Power Delivery, vol. 19, pp. 1794-1801, Oct. 2004. M. Kalantari and A. Kazemi, "Placement of distributed generation unit and capacitor allocation in distribution systems using Genetic Algorithm," in Proc. 2011 Internation Conference in Environment and Electrical Engineering, pp. 1-5. A. Elmitwally and A. Eldesouky, "An approach for placement and sizing of capacitor banks in distribution with distributed wind generation," International Transaction on Electrical Energy Systems, vol. 23, pp. 539-552, Feb. 2012. M. M. Aman, G. B. Jasmon, K. H. Solangi, A. H. A Bakar and H. Mokhlis, "Optimum Simultaneous DG and Capacitor Placement on the basis of minimization of power losses," International Journal of Computer and Electrical, vol. 5, pp. 516-522, Oct. 2013. M. N. Muhtazaruddin, J. J. Jamian, G. Fujita, M. A. Baharuddin, M. W. Wazir, and H. Mokhlis "Distribution network loss minimization via
[21]
simultaneous distributed generation coordination with network reconfiguration," Arabian Journal for Science and Engineering, in press. D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Dept. Comput. Eng. Erciyes Uni., Turkey, Tech. Rep. TR06, 2005. J. E. Hunt and D. E. Cooke, "Learning using an Artificial Immune System," Journal of Network and Computer Applications, vol. 19, pp. 189-212, Apr. 1996. M. A. Kashem, V. Ganapathy, G. B. Jasmon and M. I. Buhari, "A novel method for loss minimization in distribution network," in Proc. 2000 Electric utility Deregulation and Restructuring and Power Technologies, pp. 251-256. N. Rugthaicharoencheep, S. Nedphograw and W. Wanaratwijit, "Distribution system operation for power loss minimization and improved voltage profile with distributed generation and capacitor placement," in Proc. 2011 Electric utility Deregulation and Restructuring and Power Technologies, pp. 1185-1189. S. Gopiya Naik, D. K. Khatod and M. P. Sharma, "Optimal allocation of combined DG and capacitor for real power loss minimization in distribution networks," International Journal of Electrical Power and Energy System, vol. 53, pp. 967-973, Dec. 2013.
