PLACEMENT OF DISTRIBUTED GENERATION AND RECONFIGURATION OF RDS FOR LOSS MINIMIZATION USING GENETIC ALGORITHM
Abhay Raj Sahu1, Anmol Pandey2, Nitin Saxena3
1M.E. Student, Department of Electrical Engineering, Jabalpur Engineering College, Jabalpur 482011, India
2M.E. Student, Department of Electrical Engineering, Jabalpur Engineering College, Jabalpur 482011, India
3Assistant Professor, Department of Electrical Engineering, Jabalpur Engineering College, Jabalpur 482011, India
Abstract - This paper is about implementation of genetic algorithm to get the plan of switching operation for feeder reconfiguration, placement of DG and simultaneously reconfiguration of RDS and placement of DG in IEEE 33 bus system. The main aim is to reduce total real and reactive power losses and also improve the system voltage profile by satisfying complete distribution system constraints. Here multiple DG of type 1 and type 2 are introduced to reduce system loss and improve voltage profile. The proposed work has been performed using MATLAB R2021b programming environment on IEEE 33 bus system.
The backward forward sweep load flow method is used for base case load flow.
Keywords: Distributed Generation (DG), Reconfiguration, Genetic Algorithm, Type 1 and Type 2 Dg, Radial Distribution System (RDS).
1. INTRODUCTION
The introduction of distributed generation (DG) alters the structure of the distribution network, which has a significant impact on distribution network loss. Distributed generation (DG) have attracted considerable attention because of their potential solution for some issues such as increased power consumption and transmission capacity shortage.
Through the integration of DGs into the distribution system, the voltage profile, service restoration, uninterrupted power supply, and energy efficiency would all be improved. Here, type 1 and type 2 multiple number of DG’s is used for analysis in this paper. The real power is supplied by type 1 DG without it consume any reactive power. The reactive power is supplied with the help of type 2 DG.
One of the most important control strategies in distribution networks that might be impacted by the connectivity of DGs efficiency is distribution feeder reconfiguration (DFR)[1]. Since distribution network is classified as meshed, radial but for reconfiguration of network only radial distribution network is used[2]. Classical and heuristics technique are used for reconfiguration of network. Since from past many years it improves reliability of system and reduces outage time. Mathematical programming method are used for classical techniques[3], [4] and combinatory problem can be solved by using heuristics
technique[5], [6]. Here in this paper heuristic technique is used for network reconfiguration.
[1]is used fixed probabilities of mutation and crossover for genetic algorithm based on roulette wheel selection method for loss minimisation for PDNR. [7] Proposed a refined GA with some improvement in chromosome coding, fitness function and adaptive mutation pattern. [8] Presented a fuzzy controlled mutation with two point cross over and very high cross over probability.[9] Presented an efficient crossover mutation operator based on the information of a single loop caused by closing a normal open switch.[10]
implement genetic algorithm for DG placement on IEEE 33 bus system for loss minimisation where distribution system is consider as radial. [11]For the purpose of minimising power loss while taking 33 bus system power limits of 0-2MW and 0- 3MW into consideration, an optimal location and size of multiple DG based on a genetic algorithm is given. The load flow method calls for the creation of the matrices BIBC and BCBV. The collected results show that when DG is installed at the location determined to be the most suitable, voltage profile improvement and loss reduction occur.[12] Artificial Bee Colony (ABC) based optimal DG placement was previously proposed in this article, they used the Index Vector
Method (IVM) to obtain the best possible DG placement. In the course of optimization, the IVM value at each bus is determined to condense the search space.
This is tested on buses 15 and 33, and the results show where DG placement is practical.[13] proposed Simulated Annealing (SA), a metallurgical technique that involves allowing a metal to heat up until its melting point decreases and then allowing it to cool, is used to achieve multi-objective optimal placement of many DG in order to get the least state of energy. This idea is used to create an algorithm, which is then applied to the 33 bus test system. The Forward/Backward sweep load flow technology used here shows a reduction in losses and an improved voltage profile.
In this paper genetic algorithm is used for loss minimisation by reconfiguration of feeder, by placement of DG and simultaneously reconfigure the system and DG placement. The process has been carried out in a IEEE 33 bus system.
Further comparison of results between reconfiguration, DG placement, and DG placement and reconfiguration of network is concluded.
2. OBJECTIVE FUNCTION AND CONSTRAINTS
While reconfigure the RDS by applying the genetic algorithm, the main aim is to minimise the losses by satisfying system operating constraints. So objective function is formulated as.
Min Plosses = ni=1|Ii|2KiRi where i ∈ N Here, Ii represents current in branch I, Ri
represents resistance of branch I, N represents total number of branch, k represents topology status of the branch where Ki=1 for closing the switch and ki=0 for opening the switch.
2.1 Constraints
Radial Network Constraint: The distribution network should be made up of a radial structure considering the operational point of view.
Node Voltage Constraint: To maintain the power quality the voltage should be in a permissible limit. The variation of voltage is
±5% is allowed to satisfy the limit.
Vmin ≤Vbus ≤ Vmax
Where, Vmin = minimum bus voltage.
Vmax = maximum bus voltage.
Generator Operation Constraints:
The generator power should be in a limit for the operation.
Pimin ≤ Pg ≤ Pimax
Where,Pimin = DG lower bound Pimax = DG upper bound
Feeder Capability Limit: The current of feeder should be in limit Ii ≤ Imax
Where, Imax = maximum current 3. METHODOLOGY
3.1 Base Case Load Flow
Backward forward sweep load flow method mainly used for radial balanced distribution system.
STEPS
1. Read the line data and load data.
2. Initialization of bus voltage
Vj = Vs<0o for node j = 2, 3,…….N here N = total number of node.
3. Initialize the iteration count K =1 4. Calculation of load current.
Ij(k) = ( PLj + jQLj / Vj(k−1))* for j = 2,3,….N Here, PLj isactive power at jth bus and QLj
is reactive power at jth bus.
5. Backward sweep: Here calculate the line current by going backward form N node and calculating till reached at first node or source node.
Imnk = Ink + summation of all current of branches emitted from bus N. ……… (1) here, Imnk = current between two node at kth iteration
So above equation (1) forms a matrix and also be written as
[IB] = [BIBC] [IL]
Where IB = Branch current matrix IL = Load current matrix,
BIBC = Branch injection to branch current matrix
6. Forward sweep: Here calculate voltage in forward direction starting from source node or first node and calculating till reached at N node.
Vn(k) = Vm(k) – ZmnImn(k) ……… (2) Here, Zmn= impedence between two node.
So above equation (2) formed a matrix and also be written as
∆V = [BCBV][IB]
∆V = [BCBV][BIBC][IL]
∆V = [DLF][IL]
Here, ∆V = Difference in voltage between twonodes
BCBV = Branch current to branch voltage matrix
DLF = Distribution load flow matrix, it is a product of BCBV and BIBC matrix.
7. Calculation of error: It is calculated magnitude of error at kth iteration and (k-1)th iteration difference of voltage of jth bus.
ej(k) = |Vj(k)- Vj(k−1)|for j = 2, 3, ……N 8. Now, maximum error magnitude
obtained further compare with tolerance value if it is less than tolerance then print result, if not the go to step 3 and iteration count
incremented by 1 (i.e. k = k+1) and repeat the process till convergence is occurred.
3.2 Genetic Algorithm
GA mainly used to solve optimization problem to get global solution, it is natural selection based optimization which solves problem with the help of fitness function. The switch status ( i.e.
opening and closing of switch), DG size and DG location is taken as variable that are encoded in a binary strings, it acts like genes of chromosomes in biological system. GA takes group of chromosomes as initial population, with the help of objective function obtain fittest value by using many times genetic operation such as mutation and crossover.
NO yes
Flow chart STEPS
1. Initial Population: Start the process with initialisation of population as switch position, bus number which have highest loss for placement of DG with the help of objective function.
2. Reproduction: The fittest value obtained from objective function is gone through the process of selection of parent string i.e.
fittest switch position, fittest bus for DG placement. So fittest value
is known as parent and it makes offspring.
3. Crossover: Now, from parent strings the random position is selected then swap characters in that random position. Cross over point is represents that random position, 0.8 is the probability of crossover is assumed.
4. Mutation: The loss of genetics occurred during process of reproduction and crossover is prevented in this process, 0.08 is
Initial population
Start
stop
Evaluation of fitness
Select mate
crossover
Mutation
Convergence
the probability of mutation is assumed.
5. If convergence occurred the stop the process if not then go to step 2.
4. CASE STUDY AND RESULT DISCUSSION
The below figure shows IEEE 33 bus radial distribution system with 5tie switches 33,34,35,36, and 37 and 32
sectionalizing switches ( i.e. from 1 to 32).
The base MVA is taken as 100 and base KV is taken as 12.66, minimum voltage is 0.95 and maximum voltage is 1, maximum size of DG is 1000 KW. The maximum feeder current carrying limit is assumed to be 2000 ampere. The algorithm is performed with 30 iterations and obtained crossover rate of 0.8 and mutation rate of 0.08. The number of initial population is assumed to 6.
4.1 Power Loss Reduction Analysis Case 1:- Placement of Multiple DG’S.
By running code continuously various times in MATLAB programming environment, the lowest power loss value get, is selected. Initially the tie switches
are open i.e. from 33 to 37. The type 1 and type 2 DG is installed to reduce the real and reactive power losses respectively, as the number of DG is increases i.e. from 1 to 4 the reduction in power loss has been recorded.
Parameter Base
case Number
of DG
1 DG 2 DG 3 DG 4 DG
Type of DG - Type 1 Type 2 Type 1 Type 2 Type 1 Type 2 Type 1 Type 2
Active power
loss (KW) 202.6771 114.35 146.81 98.647 132.32 85.716 124.52 79.45 123.56 Reactive power
loss (KVAR) 135.141 73.75 95.36 67.712 86.00 57.948 82.57 53.25 81.29
Optimal DG
location - 10 15 26,15 6,4 10,30,6 30,6,5 7,16,32,26 25,5,27,
24
DG size P (KW) - 982 0 902,889 0 1000,822,
173 0 621,340,612,
690 0
DG size Q
(KVAR) - 0 373 0 672,135 0 835,299,349 112,441,
733,225 Table 1
Case 2:- Reconfiguration of Network.
By using genetic algorithm the switch opening position is chooses such that the losses has been reduced. The reduction of 25.17% of active power loss is observed
and 14.89% reactive power loss is observed by reconfiguration of network, tabular form result is shown in below table 2.
Table 2
Parameter Base case Reconfiguration % power loss reduction
Active power loss 202.677 151.65 25.17
Reactive power loss 135.141 115.01 14.89
Open tie switch number
33,34,35,36,37 32,7,27,10,12 -
Case 3:- Placement of DG and Simultaneously Reconfiguration of Network.
Here the DG location, size, and switching position is such that the power loss minimisation has been observed. Multiple DG’s of type 1 and type 2 is placed such that the real and reactive power losses is reduced. While placement of DG and simultaneously analysis of switching
position i.e. opening of switches to reduced power losses. After placement of 1 DG of type 1 and type 2 simultaneously reconfiguration of network the power real loss reduced to 86.34 KW with four number of DG of type 1 and 104.95 KW with four number of DG’s of type 2. The various result of type 1 and type 2 DG is shown in tabular form in below table 3.
Table 3 4.2 Voltage Profile Analysis
The limit of voltage profile is selected between 0.95 PU to 1.05 PU, during base case at bus 20 the minimum voltage is recorded which is 0.913056 PU, after reconfiguration the voltage is improved to 0.919749 PU and after type 1 DG placement the voltage profile is improved to 0.964559 PU and simultaneously placement of type 1 DG and reconfiguration of network the voltage improved to 0.957661 PU at 20th bus.
Now, after placement of type 2 DG the voltage is improved to 0.95 PU and simultaneously placement of type 2 DG and reconfiguration of network the voltage is improved to 0.957336 PU at 20th bus.
5. CONCLUSION
Genetic algorithm with backward forward sweep load flow method is used for analysis in this paper for different cases.
The voltage profile is also improved but main criteria is active power loss reduction. So, after placement of DG, reconfiguration of network and simultaneously placement of DG and parameter Base
case Number
of DG
1 DG 2DG 3DG 4DG
Type of
DG - Type 1 Type 2 Type 1 Type 2 Type 1 Type2 Type 1 Type 2 Active
power loss (KW)
202.677 110.19 127.63 73.76 114.99 72.17 126.30 86.34 104.95
Reactive power loss (KVAR)
135.141 91.34 108.46 59.14 94.04 51.81 83.84 67.03 74.57
Tie switch
number 33,34,35,
36,37 7,12,21,
28,29 7,9,33,
27,29 6,14,25,
10,31 28,7,30,
21,13 13,37,8,
32,33 7,13,24,
16,9 7,25,11,
21,31 7,11,15, 12,37 Optimal
DG location
- 31 22 18,29 24,5 14,29,7 12,26,7 8,17,31,
21 4,24,29,2 2 DG size P
(KW) - 400 0 716,100
0 0 483,835,
560 0 978,295
, 632,836
0
DG size Q
(KVAR) - 0 261 0 475,356 0 291,238
,112 126,392,
744,421
network reconfiguration the power loss is reduced. The maximum reduction of active power loss is obtained from placement of 4 DG’s of type 1 and simultaneously reconfiguration of network which is 61.57%.
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