ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037
Available Online: www.ajeee.co.in/index.php/AJEEE
Vol. 07, Issue 12, December 2022 IMPACT FACTOR: 8.20 (INTERNATIONAL JOURNAL) 63 OPTIMAL LOCATION OF DG WITH BINARY BAT ALGORITHM FOR VOLTAGE PROFILE
IMPROVEMENT
Bhaskar Mishra, Abhay Raj Sahu, Nitin Saxena
Jabalpur Engineering College, ―Department of Electrical Engineering
Abstract – Distributed Generation can play a significant role in modern day power system due to its rise in energy demands and other environmental and economic factors. This paper deals with DGs size and location optimizationwith an aim to reduce real power loss and enhance bus voltage profile. These parameters have been testedusing two different methods Binary Bat Algorithm and Genetic Algorithm. Type-1 and Type-3 DG based on real and reactive power infusion are considered in the present work. For both the cases performance is analysed through one as well as multiple DG units onIEEE- 33 radial bus distribution network.
Keywords: Distributed Generation (DG), Binary Bat Algorithm, Genetic Algorithm, Radial Distribution System (RDS).
1 INTRODUCTION
Distributed generation (DG) or dispersed generation[1] is the power generation at the distribution system with renewable or low carbon emission source. Although, DG by itself is not a new concept, rather some consumers have been keeping their own generation units from decades. But in the recent years DG is fast finding its place to meet the increasing power demands. There are several factors of concern which leads to ever growing demands of DGs [2]. The main factors are the environmental pollution, increasing Technical and Commercial losses, growing power quality disturbance and reliability of the network. Maturing technical know- how, competitive electric market, power electronic devices application and emergence of new distributed power technologies like fuel cells and micro- turbines has improved the efficiency, reliability and reduced cost of DG. Main research part of DG installation in Distribution system is to find the optimal location and size of DGs, which can result in power loss and cost reduction as well as improving the voltage profile of critical buses and reliability of the system [3].
There are significant number of algorithms which has been proposed to solve DG allocation problem, like Genetic Algorithm [4], Particle Swarm Optimization (PSO) [5], ant colony [6], analytical methods [7], simulated annealing [8] etc. which gives good results but still evolving. Several algorithms are modified and many hybrid algorithms are created to improve the quality of algorithm. Earlier optimization techniques were concerned to reduce only power
losses [9] in radial distribution system.
With the growing power quality problems, reliability issues and cost, multi-objective function [10] were used. Nature inspired algorithms are observed to have higher potential to solve complex problem.
Hence, several bio-inspired heurist algorithms were developed such as PSO [5], Artificial Bee Colony [11], Cuckoo Search algorithm[12], etc. In this study a new gradient free, meta-heuristic, population-based algorithm called Bat Inspired Algorithm[13] is described to evaluate the optimal location and sizing of DGs. This algorithm is inspired by echolocation behaviour of microbats and has potential to solve complex problem in easier way. In this approach an IEEE 33 bus system is considered as base case for further study. The simulation results show that Binary Bat Algorithm is more precise in terms of quality of solution.
2 BINARY BAT ALGORITHM
A binary search space is similar to a hypercube in whichthe search agents of the optimization algorithm makes use of binary logic to move to either closer or farther corner of this hypercube. Hence, in order to design the binary version of BA, the artificial bats can move around the search space by utilizing position and velocity vectors within the continuous real domain. In discrete binary space, the position updating means switching between ―0‖ and ―1‖. The velocities of search agents is used for this purpose. It is required to define a transfer function that defines a transformation probability from 0 to 1 for a position vector element
ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037
Available Online: www.ajeee.co.in/index.php/AJEEE
Vol. 07, Issue 12, December 2022 IMPACT FACTOR: 8.20 (INTERNATIONAL JOURNAL) 64 and vice versa. The movement of the
particle in a binary space is in accordance with this transfer function.
The particles having higher velocities are prioritized to change their position.The following equations show the transfer function and position updating expression for the given case.
𝑉 𝑣𝑖𝑘 𝑡 = 2
𝜋arctan 𝜋
2𝑣𝑖𝑘 (1)
𝑥𝑖𝑘(𝑡 + 1) = (𝑥𝑖𝑘(𝑡))−1, 𝑟𝑎𝑛𝑑 < 𝑉(𝑣𝑖𝑘(𝑡 + 1) 𝑥𝑖𝑘(𝑡), 𝑟𝑎𝑛𝑑 ≥ 𝑉(𝑣𝑖𝑘(𝑡 + 1)
(2)
Where xik(t) and vik(t) indicate the position and velocity of the ith particle at tth iteration in kth dimension and 𝑥𝑖𝑘(𝑡))−1 is the complement of 𝑥𝑖𝑘(𝑡).
3 GENETIC ALGORITHM
In order to find a high-quality, nearly global solution, GA uses the principles of natural evolution and population genetics.
Three genetic processes—selection or reproduction, crossover, and mutation—
are used in binary GA to change a population of bit strings. A chromosome is a group of strings that each indicates a potential answer. The algorithm begins with a randomly generated initial population. The genetic operations are used to create a new generation while taking into account the optimization problem's solution's fitness. Typically, the string's fitness in a minimization issue is the inverse of the string's objective function. Through several generations of iterations, solutions' fitness is increased.
A backward forward load flow calculation is carried out for every chromosome in the generation.
4 PROBLEM FORMULATION AND IMPLEMENTATION
In the present work, the objective function has been considered as the minimization of power loss. This can be represented in the form of equation as follows:
min 𝑃𝑙𝑜𝑠𝑠 = 𝑚𝑖=1𝐼𝑖2𝑅𝑖 (3) Where m = number of branches.
Subjected to constraints:
Power limit of DG: The DGs connected in the network must have a power limit ranging from a minimum value up to a maximum value.
𝑃𝑚𝑖𝑛 ≤ 𝑃𝐷𝐺 ≤ 𝑃𝑚𝑎𝑥 (4) The voltage limit at each and every bus should not go beyond a certain limit as below
𝑉𝑚𝑖𝑛 ≤ 𝑉𝑖 ≤ 𝑉𝑚𝑎𝑥 (5) There should be the current limit to ensure the thermal stability of the feeder as
𝐼𝑖 ≤ 𝐼𝑖𝑟𝑎𝑡𝑒𝑑 (6) The proposed algorithm implementation can be described in following steps:
Step 1: Line data and bus data is initialized.
Step 2: Base case load flow is done by Backward Forward Sweep method.
Step 3: The number and type of DG to be installed is selected.
Step 4: The power factor for the DG is chosen. It is 1 for type 1 and 0.8 for type 3.
Step 5: The range for the size of DG is set in KW.
Step 6: The load flow analysis is done through Binary Bat Algorithm and GA with power loss as objective function.
Step 7: The random value of position and size is chosen for DGs.
Step 8: The minimum value of power loss among all the iterations is chosen as the optimum and with respect to that position and size of DG.
Step 9: Power losses for different numbers and types of DGs are compared with the base case to compute the efficiency. Voltage profile graph corresponding to that is displayed.
5 RESULTS AND DISSCUSSION
The present work has been carried out using MATLAB 2021b programming environment. IEEE 33 Bus test system is used to obtain results. The programme has been run 20 times to obtain minimum losses for each case of number of DGs. There are two types of DGs used in the proposed work:
Type 1: It supplies only active power and its power factor is 1.
Type 3: It supplies both active and reactive power and power factor is taken as 0.8.
The result for both the cases with different number of DGs is shown in Table I and Table II respectively. The Table III represents the comparison between the two methods and voltage profile graph for both the cases is shown in figure 1 and
ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037
Available Online: www.ajeee.co.in/index.php/AJEEE
Vol. 07, Issue 12, December 2022 IMPACT FACTOR: 8.20 (INTERNATIONAL JOURNAL) 65 figure 2 respectively. The results for both
methods BAT and GA with reconfiguration for type 1 DG are compared in Table III.
This clearly shows that Binary BAT is more efficient than GA with configuration for the different number of DGs of Type 1.
Table I
SIMULATION RESULT FOR TYPE-1 DG
Table II
Simulation Result for Type-3 DG CASE DG Capacity
(KW) DG Capacity
(KVAR) Total DG
Size (KW) Power loss
(KW) Power loss
(KVAR) Position
of DG Efficiency (%)
NO DG --- --- 202.67 135.14 ---
1 DG 938.3169 703.73 703.73 80.45 54.62 30 60.3055
2 DG 802 715 601.5 536.2
1137.7 46.64 34.09 33 16 76.9849 3 DG 581.52 470.60
721.33
436.14 541 352.95
1130.09 38.73 26.84 24 31 10 80.8887
4 DG 882 507 10 848 661.5 380.25 682.5 636
2360.25 26. 11 20.31 2 23 32 11 87.1126
Table III
Comparison of Power Loss for Type-1 DG for Both Algorithms ALGORITHM NO DG 1DG 2 DG 3 DG 4 DG
BINARY BAT 202.67 114.12 94.91 80.19 76.27
GA 202.67 114.35 98.64 85.71 79.45
Figure 1 The voltage profile graph for Type 1 DG
Figure 2 The voltage profile graph for Type 3 DG
6 CONCLUSION
In the present paper two different methods Binary Bat Algorithm and GA are used to analyse the power loss and voltage profile. For this purpose, the IEEE 33 bus test system has been chosen for type 1 and type 3 DGs. The maximum number of DGs used is 4 in each case.
For type 1 DG the maximum efficiency of 62.36% is obtained with 4 DGs while in the case of type 3 DG maximum efficiency is 87.11% with 4 DGs. This implies that type 3 DG is more efficient in loss reduction. Thus, by increasing the number of DGs we also have more penetration of DG in the system along with loss reduction. Comparison of both the methods for type-1 DG shows that the Binary Bat Algorithm is more efficient in terms of power loss than the Genetic Algorithm.
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ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING Peer Reviewed and Refereed Journal, ISSN NO. 2456-1037
Available Online: www.ajeee.co.in/index.php/AJEEE
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