Distributed Generation Effect on Distribution System

Distributed Generation Effect on Distribution System

Mercy Rosalina KotapuriRajesh Kumar Samala 

Department of Electrical and Electronics Engineering, VFSTR (Deemed to be University), Vadlamudi, Guntur 522213, India

Corresponding Author Email: 
kmr_eee@vignan.ac.in
Page: 
155-163
|
DOI: 
https://doi.org/10.18280/jesa.540118
Received: 
28 May 2020
|
Revised: 
2 January 2021
|
Accepted: 
12 January 2021
|
Available online: 
28 February 2021
| Citation

© 2021 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).

OPEN ACCESS

Abstract: 

The idea about this proposed work, to know the Distributed Generation (DG) impact on distribution scheme. This is to improve the performance of the system using power loss reduction and voltage development. In this proposed work Wind Turbine (WT) and Photo-Voltaic (PV) units were taken for DGs and various algorithms are tested to get the effect of DG on network. In this paper one new hybrid algorithm is proposed to have optimal size and location of various types of DGs. Initially, active and reactive power losses of the test system and voltage at every bus of the test system were examined using Back and Forward (B/FW) Sweep technique. Similarly, Gravitational Search Analysis (GSA), BAT Analysis (BA) and Ant Lion Optimization (ALO) techniques were utilized to examine the parameters of the same test system. Finally, all the constraints were compared with projected hybrid approach. All the algorithms have tested on IEEE-33 and IEEE-69 standard test systems. Furthermore, the MATLAB simulation is used to get the optimal allocation of DGs.

Keywords: 

distributed generation, gravitational search analysis, BAT analysis, ant-lion optimization, power loss, optimal location, capacity

1. Introduction

The world wide apprehensions about the atmosphere, associated along with development of techniques to link non-conventional energy resources to grid and electric power market deregulation of has abstracted the distribution planners concentration towards distributed generation (DG) connection to grid-connected [1, 2]. DG known as a small-scale electrical energy generation for the requirement of sustaining station load dissimilar from the conventional or Central power station [3]. Majority of DG sources are considered with the help of green energy that is assumed pollution free. Penetration of have many technical advantages.

Furthermore, DG is accessible in modular unit, considered by easy of identify location for small generators, little construction times, and minimum capital investment [4, 5]. The technical advantages of DG include improvement of voltage, reduction in loss of power, relieved from transmission and distribution congestion, enhanced network reliability and quality of power. All the above are aids to accomplished by introducing DGs at correct sites with correct capacity otherwise, it could lead to adverse effects like augmented power losses [6, 7]. The wrong placement will leads to raise in scheme losses and sometimes it may even collapse the entire scheme [8]. Though, the tasks of detecting the optimal sizes and sites of DG units in distribution scheme are not easy [9].

Now the DG installation is placing a big vital role in distribution schemes due to its advantages over the methods in reduction in network total power loss, reduction in total operating power of the network, user friendly and environmental friendly, increase in system voltage, and reliability [10, 11]. The placement of DG is purely the choice of its owners and also investors. It also depends on location and the availability of the fuel and condition of climate [12, 13]. Though the introduction and the modifications depend on number of DGs installation like one or single DG installation and installation of multi-DGs. Various techniques have presented to find the optimal site and capacity of the DG [14-17]. Besides the reduction in power loss, the DG location is may be on the basis of reduction in cost. The mixed integer linear program, Tabu Search (TS), Genetic Approach (GA), Particle Swarm Optimization (PSO) approach, Ant Colony Optimization (ACO) and direct search approach are utilized to calculate the best DG location and siting. In those papers when introducing DGs emphasis is provided on the reduction in line loss [18-20]. Consequently, the optimization approaches would be engaged for deregulation of power industry, permitting for the optimal allocation of the DG. In the work, an efficient approach is projected to validate the load flow issue and the placement issue of DG.

2. Objective Functions

Figure 1 represents a simple two bus system. This work is to examine the suitable size and site of DG. The main objective in this research is enhancement of voltage and minimization of power losses.

Figure 1. Single line layout of two bus scheme

$VoltageDeviationIndex=\sum\limits_{i}^{N}{\frac{\left| {{V}_{rated-{{V}_{i}}}} \right|}{{{V}_{rated}}}}$       (1)

where, Vrated be the network rated value of voltage and it is 1.0 p.u. Vi be the ith bus voltage in p.u. N be the network total buses number.

Minimization of real power loss $=\left(\begin{array}{l}N_{b u s} \\ \sum_{i=2}\left(P_{g n i}-P_{d n i}-V_{m i} V_{n i} Y_{m n i} \cos \left(\delta_{m i}-\delta_{n i}+\theta_{n i}\right)\right)\end{array}\right)$       (2)

where, Pgni is generator output active power at bus ni, Pdni is the active power demand at bus ni, Vmi be the voltage of bus mi, Vni be the voltage of bus ni, Ymni be the admittance magnitude among mi bus and bus ni, δmi be the voltage phase angle at bus mi, δni be the voltage phase angle at bus ni, θni be the angle of admittance of Yi=Yni∟θni. Nbus represents number of buses in given test scheme, Ni is receiving bus number (Ni = 2, 3, Nn) and mi is the bus number that sending power to bus ni (m2 = n1 = 1) and i is the branch number that fed bus ni.

2.1 Constraints

$\begin{align}  & {{P}_{Gi}}-{{P}_{Di}}-{{V}_{i}}\left( \sum\limits_{j=1}^{{{N}_{bus}}}{\left( {{V}_{j}} {{Y}_{ij}} cos({{\delta }_{j}}-{{\delta }_{i}}+{{\theta }_{ij}}) \right)} \right)=0 \\ & {{Q}_{Gi}}-{{Q}_{Di}}-{{V}_{i}}\left( \sum\limits_{j=1}^{{{N}_{bus}}}{\left( {{V}_{j}} {{Y}_{ij}} sin({{\delta }_{j}}-{{\delta }_{i}}+{{\theta }_{ij}}) \right)} \right)=0    \\ &  i = 1, 2, ...., Nbus \\\end{align}$       (3)

Here PGiand QGistates active and reactive power generated at i bus respectively; PDiand QDistates active and reactive load at i bus respectively, Piand Qistates active and reactive injected power at i bus, Yijand θijstates the amplitude of admittance and branch voltage angle connecting i and j buses.

2.1.1 Voltage limits

For this paper work, the deviation of voltage is definite from 1.05 pu and 0.90 pu.

${{V}_{\max }}\ge V\ge {{V}_{\min }}$      (4)

Here: Vmax be the peak voltage of bus and Vmin be the lowest voltage of bus.

2.1.2 Real power loss constraint

$P{{L}_{withoutDG}}\ge P{{L}_{withDG}}$      (5)

2.1.3 DG constraint

$\sum\limits_{i=1}^{Nbus}{{}}{{P}_{Di}}\ge {{P}_{DG}}\ge {{0}_{{}}}$      (6)

$\sum\limits_{i=1}^{Nbus}{{}}{{Q}_{Di}}\ge {{Q}_{DG}}\ge {{0}_{{}}}$      (7)

where, PDiand QDiare the active and reactive load demand at the same bus.

3. B/W AND F/W Sweep Approach Formulation

Algorithm:

  1. Get line information that consists of line resistance and line reactance and Bus information including real and reactive powers at each bus.
  2. Read base values such as base KV and base MVA.
  3. Convert the load impedance into per unit values.
  4. Start Backward sweep analysis i.e. the analysis starts from the destination node to source node. This backward sweep analysis is used to determine real and reactive powers and the voltages of all the buses. The equations are as follows:

${{V}_{i}}={{V}_{i+1}}+conj(({{P}_{i+1}}+j{{Q}_{i+1}})/{{V}_{i+1}})$      (8)

${{P}_{i}}={{P}_{i\_Load}}+{{P}_{i\_Loss}}$      (9)

${{Q}_{i}}={{Q}_{i\_Load}}+{{Q}_{i\_Loss}}$      (10)

${{P}_{i\_Loss}}={{(({{P}_{i+1}}^{2}+{{Q}_{i+1}}^{2})/{{V}_{i+1}}^{2})}^{{}}}*{{R}_{i}}^{{}}$       (11)

${{Q}_{i\_Loss}}={{(({{P}_{i+1}}^{2}+{{Q}_{i+1}}^{2})/{{V}_{i+1}}^{2})}^{{}}}*{{X}_{i}}^{{}}$       (12)

where, Vi is the node current voltage, Vi+1 is voltage at next node, Pi+1 is next node active power, Qi+1 is next node reactive power, Ri is the line resistance among i and i+1 node and Xi is the line reactance line among i and i+1 node.

  1. Now check for criterion of Convergence as follows:

${{\varepsilon }_{{}}}\ge {{V}_{calculated}}-{{V}_{described}}$      (13)

Here ε specified the tolerance.

  1. If the system is under the tolerance limits i.e. the system converged, then goes to step 11. Otherwise then start Forward sweep analysis as step 7.
  2. The Forward sweep analysis starts at source node and by finding active and reactive power loss and the voltages at all the busses reach the destination node. The equations for Forward sweep analysis as follows:

${{V}_{i+1}}={{V}_{i}}+conj(({{P}_{i}}+j{{Q}_{i}})/{{V}_{i}})$       (14)

${{P}_{i}}={{P}_{i\_Load}}+{{P}_{i\_Loss}}$         (15)

${{Q}_{i}}={{Q}_{i\_Load}}+{{Q}_{i\_Loss}}$        (16)

${{P}_{i\_Loss}}={{(({{P}_{i}}^{2}+{{Q}_{i}}^{2})/{{V}_{i}}^{2})}^{{}}}*{{R}_{i}}^{{}}$      (17)

${{Q}_{i\_Loss}}={{(({{P}_{i}}^{2}+{{Q}_{i}}^{2})/{{V}_{i}}^{2})}^{{}}}*{{X}_{i}}^{{}}$      (18)

  1. Now again check the criterion of Convergence as mentioned above.
  2. If the system is under the tolerance limits i.e. the system is converged then go to step 11. Otherwise go to step 4.
  3. Print total power losses and voltages of all the busses in the system.
  4. Stop.
4. Power Flow Modelling of DGs

DGs were considering as generator buses and load buses in the case of power flow studies. The DG should reach their reacive power constraint when DG is considered as generator bus and finally this generator bus converts into load bus.

In this case, DG generates fixed real power and reactive power [21]. Hence, real power load and reactive power load at the interconnected bus (PL and QL) are changed as given in Eqns. (19) and (20),

${{P}_{L}}(t)={{P}_{L}}(t)-{{P}_{DG}}(t)$     (19)

${{Q}_{L}}(t)={{Q}_{L}}(t)-{{Q}_{DG}}(t)$      (20)

Now, this multi-objective function is for reduction in real power losses and voltage improvement at all buses. Classification of DG sources as 4 – types [22].

Type1:  Active power injecting only.

Type2:  Reactive power injecting only

Type3:  Injecting both powers active and reactive.

Type4: ​ Injecting active but consuming reactive power.

5. Proposed Approach

The projected methodology is an effective method for getting the finest capacity and site the DG. This supirior methodology is a hybridization of BA and ALO to improve the outcome of the test scheme. The ALO approach is employed to estimation of loss of real power. The outcome of the ALO approach is having high iteration to achieve the better outcome and poor performance. To enhance the performance of ALO which is based on the BAT approaach. The process of the projected technique is presented in subsequent section and the flow chart for the proposed method is shown in Figure 2.

Figure 2. Proposed hybrid technique flow chart

6. Results

This projected scheme is worked on MATLAB. Outcome is tested on IEEE-33 which have of 33 buses and 32 branches with 12.66 kV and 100 MVA are base values and 3.715 MW is complete real and 2.3 MVAR is complete reactive power load shown in Figure 3 and IEEE-69 which have of 69 buses and 68 branches with 12.66 kV and 100 MVA are base values and 3.80219 MW is complete real and 2.6946 MVAR is total reactive power load shown in Figure 4, Power factor of this scheme is 0.85 lagging, 0.85 leading and Unity. For the implementation purpose, projected hybridization technique and traditional scheme parameters are given in Table 1.

Figure 3. IEEE-33 standard test system

Figure 4. IEEE-69 test bus

Table 1. Implementation parameters

Description

Algorithms

Values

Population size (n)

BA

20

Number of generations (N)

10

Loudness (A)

1

Pulse rate (r)

1

Frequency (Q)

(0,2)

Dimension (d)

4

Ant lion pits

ALO

3

Number of Ant lions

10

Max iteration

100

In this paper, two DGs has considered for better site and sized. All the objecctives like DG capacity, DG site, loss of active power, cost of power loss, cost of DGs and Voltage Stability Index (VSI) when the power factor is unity, 085 lagging and 0.85 leading using various methods of algorithms are reported in Tables 2 to 5. In Table 6 comparison analysis of power loss and VSI are mentioned. In Table 7 & 8 cost of Power Loss validation per year for IEEE-33 and IEEE-69 bus scheme has done. In Tables 9 & 10 cost of DG validationn per annum for IEEE-33 bus and IEEE-69 bus scheme has done.

Table 2. Optimal location and capacity of DG using GSA method on IEEE-33 & 69 Bus

Bus No

Normal Power Loss (kW)

Power loss (kW)

% Reduction

DG capacity (kW)

Optimal bus connected for DG

Load connected Bus

Load Power Loss

GSA Power Loss

PV

WT

PV

WT

33

210.016

220.7793

207.6548

5.94

12

204

4

19

24

225.8542

200.4888

11.23

85

190

2

27

31

223.8893

198.9805

11.12

100

196

22

6

29

220.0239

201.2303

8.54

60

193

3

4

7

69

237.9656

241.4972

226.566

6.18

12

205

20

20

10

238.2408

232.6279

2.35

85

160

48

24

48

386.3267

230.712

40.28

100

153

12

30

60

261.2378

227.2329

13.01

60

165

12

14

63

Table 3. Optimal location and capacity of DG using BAT method on IEEE-33 & 69 Bus

Bus No

Normal Power Loss (kW)

Power loss (kW)

% Reduction

DG capacity (kW)

Optimal bus connected for DG

Load connected Bus

Load Power Loss

BAT Power Loss

PV

WT

PV

WT

33

210.016

220.7793

206.5204

6.45

12

160

20

14

24

225.8542

172.3606

23.68

85

190

16

16

31

223.8893

170.8101

23.70

100

179

18

31

29

220.0239

180.0811

18.15

60

172

10

17

7

69

237.9656

241.4972

226.7515

6.10

12

209

24

26

10

238.2408

224.9875

5.56

85

158

54

19

48

386.3267

224.867

41.79

100

196

53

68

60

261.2378

193.4544

25.94

60

204

61

58

63

Table 4. Optimal location and capacity of DG using ALO method on IEEE-33 & 69 Bus

Bus No

Normal Power Loss (kW)

Power loss (kW)

% Reduction

DG capacity (kW)

Optimal bus connected for DG

Load connected Bus

Load Power Loss

ALO Power Loss

PV

WT

PV

WT

33

210.016

220.7793

183.806

16.74

12

163

18

17

24

225.8542

177.209

21.53

85

156

16

11

31

223.8893

150.4867

32.78

100

198

12

18

29

220.0239

183.7687

16.47

60

169

9

11

7

69

237.9656

241.4972

236.312

2.14

12

178

45

58

10

238.2408

187.3165

21.37

85

217

62

59

48

386.3267

212.3519

45.03

100

216

8

60

60

261.2378

218.3531

16.41

60

196

20

60

63

Table 5. Optimal location and capacity of DG using proposed method on IEEE-33 & 69 Bus

Bus No

Normal Power Loss (kW)

Power loss (kW)

% Reduction

DG capacity (kW)

Optimal bus connected for DG

Load connected Bus

Load Power Loss

Proposed Power Loss

PV

WT

PV

WT

33

210.016

220.7793

173.856

21.25

12

153

18

7

2

225.8542

167.009

26.05

85

150

16

15

12

223.8893

138.2429

38.25

100

135

12

10

14

220.0239

156.4589

28.89

60

162

19

25

25

69

237.9656

241.4972

216.256

10.45

12

158

40

55

6

238.2408

166.8459

29.96

85

207

60

59

9

386.3267

155.4853

59.75

100

166

18

62

52

261.2378

202.6521

22.42

60

176

25

60

63

Table 6. Comparison analysis of power loss and VSI

Methods

Power Loss (kW)

VSI (p.u)

Proposed Method

138.25

0.64451

ALO

150.55

0.9847

BAT

180.57

0.9578

GSA[24] method

182.19

0.9364

IWD[23]

185.78

0.9155

BFOA [23]

186.48

0.9047

Multi objective particle swarm optimization (MOPSO) [23]

194.25

0.87

Particle Swarm Optimization (PSO) [23]

204.78

0.81

Genetic Algorithm (GA) [23]

208.65

0.72

Table 7. Cost of power loss comparison per year for IEEE 33 bus

Hour

Base power loss cost in Rs

Load power loss cost in Rs

GSA power loss cost in Rs

BAT power loss cost in Rs

ALO power loss cost in Rs

ALO_BAT power loss cost in Rs

1

8517998

8923905

7373983

8440513

7391728

7046572.67

2

8517998

8923905

8195503

7308062

8476607

7265525.06

3

8517998

8923905

7333742

7528798

7813107

7257944.75

4

8517998

8923905

7661734

7517226

7727067

7441914.68

5

8517998

8923905

8186519

7561682

7522080

7486709.14

6

8517998

8923905

7512772

7492773

7418815

7274989.49

7

8517998

8923905

8493392

8498914

8491055

8489011.77

8

8517998

8923905

8451636

8449676

8448817

8425020.6

9

8517998

8923905

8439029

8229026

8404840

8180354.81

10

8517998

8923905

7986251

8046167

8113817

7499009.4

11

8517998

8923905

8330884

8473763

8000282

7473762.56

12

8517998

8923905

7987481

8473763

8332954

7473762.56

13

8517998

8923905

8156772

8063823

8172119

8061563.15

14

8517998

8923905

8439029

8405220

8502651

8229026.32

15

8517998

8923905

8470716

8511538

8470489

7514878.21

16

8517998

8923905

8489433

8496191

8516868

8508245.54

17

8517998

8923905

7683846

8482849

7696048

7257115.29

18

8517998

8923905

7497171

8477682

7544748

7378233.86

19

8517998

8923905

8434907

8475967

7506600

7323525.38

20

8517998

8923905

8204306

7549351

7396649

7242864.13

21

8517998

8923905

7813055

7403818

8299594

7378969.95

22

8517998

8923905

8051446

7487396

7892847

7481511.07

23

8517998

8923905

7761192

8205383

7649459

7212076.36

24

8517998

8923905

7558257

7734516

8104634

7711234.14

Table 8. Cost of power loss comparison per year for IEEE 69 bus

Hour

Base power loss cost in Rs

Load power loss cost in Rs

GSA power loss cost in Rs

BAT power loss cost in Rs

ALO power loss cost in Rs

ALO_BAT power loss cost in Rs

1

9651600

9651600

9671586

9219854

9229448

7244573.81

2

9651600

9651600

9356107

9299953

9652663

7632597.48

3

9651600

9651600

9652362

9664497

9339775

7546746.19

4

9651600

9651600

9268497

9278224

9411663

7626895.48

5

9651600

9651600

9350876

9653844

9247944

8217111.47

6

9651600

9651600

9664783

9651392

8538077

7794949.59

7

9651600

9651600

9651579

9648141

9641183

8508245.54

8

9651600

9651600

9651586

9619344

9619679

8502077.95

9

9651600

9651600

9648673

9530788

9500202

8144125.95

10

9651600

9651600

9492634

9443047

9441862

8200184.15

11

9651600

9651600

9651486

9651749

9461584

8473762.56

12

9651600

9651600

9465580

9455369

9461584

8469388.97

13

9651600

9651600

9653613

9001161

9470669

8007547.99

14

9651600

9651600

9529950

9499182

9651601

8404839.71

15

9651600

9651600

9651533

9635178

9632185

8498646.59

16

9651600

9651600

9651539

9638333

9611790

8505460.77

17

9651600

9651600

9652089

9328034

9292236

8189127.06

18

9651600

9651600

9313432

9302098

9632466

8309651.13

19

9651600

9651600

9235937

9650583

9234193

7916035.42

20

9651600

9651600

9260795

9279799

9434449

7568355.16

21

9651600

9651600

9650720

9362206

9260210

8469507.36

22

9651600

9651600

9651427

9663745

9663513

7631668.76

23

9651600

9651600

9653771

9304664

9651274

7716827.45

24

9651600

9651600

9306660

9422985

9312535

7592195.86

 

Table 9. Cost of DG comparison per year for IEEE 33 bus

Hour

Cost of DG GSA

Cost of DG BAT

Cost of DG ALO

Cost of DG ALO_BAT

1

7575926

7.71E+06

7866419

33.1186

2

8517998

8506202

8510348

491.1524

3

7724767

7468376

7357465

461.6039

4

8478970

7651560

7481571

355.3109

5

7769345

8125602

7723558

577.6897

6

8242439

7794950

7373983

556.5678

7

7697305

8456816

8181609

1.4901

8

7679697

8478540

8306350

34.5988

9

8119172

8193582

8489999

199.2426

Hour

Cost of DG GSA

Cost of DG BAT

Cost of DG ALO

Cost of DG ALO_BAT

10

7640147

8060989

7631800

221.9176

11

8070763

8448372

8048931

301.9428

12

7987481

8387257

8495706

209.9608

13

8051200

7961421

8112656

200.5902

14

7761041

8370566

7464349

198.1552

15

8512047

8511112

8495152

3.454

16

7308774

7561328

7284663

14.6157

17

8517998

8497279

8388254

24.4005

18

7294707

8155235

7835034

533.5672

19

8161740

7642903

8229158

438.9456

20

7528630

7546746

8441886

496.2231

21

7244574

7627491

7823310

469.245

22

7724767

8303362

7481328

378.4716

23

7715357

8215940

8279658

158.3316

24

8003379

7439767

8238586

441.3625

 

Table 10. Cost of DG comparison per year for IEEE 69 bus

Hour

GSA DG cost

BAT DG cost

ALO DG cost

ALO_BAT DG cost

1

9.7747

84.8218

260.787

6.1849

2

57.4047

65.4217

96.8956

258.8895

3

111.2024

88.9083

101.0712

107.1286

4

81.1755

20.6007

94.1843

2.7076

5

87.9584

74.3554

86.7322

75.2184

6

91.5814

2.4872

1.3107

81.4918

7

0.25629

2.799

0.29865

0.26301

8

7.6965

5.0608

6.6971

0.28104

9

3.8551

9.1351

29.0238

0.41392

10

40.6819

41.885

133.5955

33.7049

11

49.2409

2.1102

47.526

54.0138

12

49.2409

57.8351

1.2625

1.9995

13

49.5119

0.95907

40.6819

12.4266

14

0.32479

36.0366

24.0083

0.39216

15

6.6971

19.8897

0.26118

19.8897

16

2.7966

2.799

1.0334

0.30088

17

64.5767

102.6642

1.1716

107.5617

18

82.561

310.2486

72.0044

1.6327

19

107.8643

4.7584

1.2331

112.154

20

87.486

87.5545

9.976

86.3331

21

1.5051

301.2334

1.5834

7.8534

22

74.8819

0.70593

86.6014

101.4023

23

5.3722

9.9285

109.9

23.9401

24

78.3951

74.7885

90.0874

2.9012

Figure 5. Comparison analysis of voltages using various methods for 24 hours on IEEE-33 bus system

The Comparison Analysis of load line power loss using various methods connected in different DGs from different time period is illustrated in Figures 5 to 12. From the simulation outcomes it may be observed that low active power losses and improved voltage at each bus was achieved by the connection of DGs at the optimal place obtained by VSI using various approaches. Overall loss reduction is accomplished with the integration of DGs utilizing the projected technique which is much superior than the power loss of different techniques. Hence, this can be concluded that improved ALO technology is much effective than other technologies in reduction of power loss of IEEE-33 and IEEE-69 standard radial distribution system.

Figure 6. Comparison analysis of loss of power using various methods for 24 hours on IEEE-33 bus system

Figure 7. Comparison analysis of cost of loss of power using various methods for 24 hours on IEEE-33 bus system

Figure 8. Comparative analysis of cost of DG various methods for 24 hours on IEEE-33 bus system

Figure 9. Comparison analysis of voltages using various methods for 24 hours on IEEE-69 bus system

Figure 10. Comparative analysis of cost of DG using various methods for 24 hours on IEEE-69 bus system

Figure 11. Comparison analysis of loss of power using various methods for 24 hours on IEEE-69 bus system

Figure 12. Comparison analysis of cost of loss of power using various Methods for 24 hours on IEEE-69 bus system

7. Conclusion

Here, the DG in a RDS is connected in an optimal location and capacity, which is estimated based on the efficient technique. This technique is improved ALO algorithm, which is the performance is improved by using the Bat algorithm. The projected approach is capable of provide much competitive outcomes in terms of updated exploration, local optima avoidance, exploitation and convergence. The ALO approach is also determine best optimal approaches for the majority of classical engineering issues employed, proving that this approach has advantages in solving constrained issues with diverse search spaces. Then the performance of the ALO is improved with the help of BA. The reactive power capability of PV network, wind turbines are considered in the voltage control. To evaluate the performance of the proposed system, an ALO technique based on the BA is also performed to obtain the best reactive power output of the DGs. The proposed control network is also applied to the IEEE-33 and IEEE-69 distribution scheme to show the robustness of the projected technique. The outcomes proves that the projected approach is much effective, and has a better fitness function, and has comparable time of convergence with GSA and capability to handle complex optimization issues. In addition, projected approach is much efficient in terms of loss minimization, voltage enhancement and load ability enhancement of distribution scheme. The efficiency of the projected technique is determined and compared with the existing techniques.

Nomenclature

Nn

Numbaer of system buses

Vni

specifies the ith bus voltage

Vrated

rated bus voltage

Pgni

generator output real power at ni bus

Pdni

demand of real power at ni bus

Vmi

voltage of mi bus

Vni

voltage of ni bus

Ymni

admittance magnitude among mi bus and ni bus

Nbus

number of buses

ni

receiving bus number

mi

bus number that sending power

i

branch number that fed ni bus

PGi

real power generated at bus i

QGi

reactive power generated at bus i

PDi

real load demand at bus i

QDi

reactive load demand at bus i

Pi

real injected power at bus i

Qi

reactive injected power at bus i

Yij

magnitude of admittance

Vmax

peak voltage at bus

Vmin

lowest voltage at bus

Vi

current node voltage

Vi+1

voltage at next bus

Pi+1

next node active power

Qi+1

next node reactive power

Ri

line resistance among i and i+1 node

Xi

line reactance line among i and i+1 node

Greek symbols

δmi

voltage phase angle at mi bus

δni

voltage phase angle at ni bus

θni

angle of admittance

θij

branch voltage angle

Subscripts

DG

Distributed generation

WT

Wind turbine

PV

Photo-voltaic

BW/FW

Backward and farward sweep

GSA

Gravitational search algorithm

BA

Bat algorithm

ALO

Ant-lion optimization

AI

Artificial intelligance

OPF

Optimal power flow

kW

Kilo watt

p.u

Per unit

MOPSO

Multi-objective particle swarm optimization

PSO

Particle swarm optimization

GA

Genetic algorithm

VSI

Voltage stabilty index

  References

[1] Biswas, S., Goswami, S.K., Chatterjee, A. (2012). Optimum distributed generation placement with voltage sag effect minimization. An International Journal of Energy Conversion and Management, 53(1): 163-174. http://doi.org/10.1016/j.enconman.2011.08.020

[2] Muttaqi, K., Le, A.D., Negnevitsky, M., Ledwich, G. (2014). An algebraic approach for determination of DG parameters to support voltage profiles in radial distribution networks. IEEE Transactions on Smart Grid, 5(3): 1351-1360. http://doi.org/10.1109/TSG.2014.2303194

[3] Moradi, M.H., Abedini, M. (2012). A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. An International Journal of Electrical Power and Energy Systems, 34: 66-74. http://doi.org/10.1109/IPECON.2010.5697086

[4] Sultana, U., Khairuddin, A.B., Aman, M.M., Mokhtar, A.S., Zareen, N. (2016). A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system. An International Journal of Renewable and Sustainable Energy Reviews, 63: 363-378. http://doi.org/10.1016/j.rser.2016.05.056

[5] Reddy, S.C., Prasad, P.V.N., Laxmi, A.J. (2012). Reliability improvement of distribution system by optimal placement of DGs using PSO and neural network. In proceedings of International Conference on Computing, Electronics and Electrical Technologies [ICCEET]. http://doi.org/10.1109/ICCEET.2012.6203836

[6] Jabr, R.A., Pal, B.C. (2009). Ordinal optimisation approach for locating and sizing of distributed generation. IET Generation, Transmission, Distribution, 3(8): 713-723. http://doi.org/10.1049/iet-gtd.2009.0019

[7] Priya, R., Prakash, S. (2014). Optimal location and sizing of generator in distributed generation system. An International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering 2(3): 1-5.

[8] Nayanatara, C., Baskaran, J., Kothari, D.P. (2016). Hybrid optimization implemented for distributed generation parameters in a power system network. An International Journal of Electrical Power and Energy Systems, 78: 690-699. http://doi.org/10.1016/j.ijepes.2015.11.117

[9] García, J.A.M., Mena, A.J.G. (2013). Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm. An International Journal of Electrical Power and Energy Systems, 50: 65-75. http://doi.org/10.1016/j.ijepes.2013.02.023

[10] Abu-Mouti, F.S., El-Hawary, M.E. (2011). Heuristic curve-fitted technique for distributed generation optimisation in radial distribution feeder systems. IET Generation, Transmission, Distribution, 5(2): 172-180. http://doi.org/10.1049/iet-gtd.2009.0739

[11] Bharathi Dasan, S.G., Selvi Ramalakshmi, S., Kumudini Devi, R.P. (2009). Optimal siting and sizing of hybrid distributed generation using EP. In proceedings of 3rd International Conference on Power Systems, Kharagpur, India. http://doi.org/10.1109/ICPWS.2009.5442761

[12] Mahipal, B., Naik, G.B., Kumar, C.N. (2016). A novel method for determining optimal location and capacity of dg and capacitor in radial network using weight-improved particle swarm optimisation algorithm (WIPSO). An International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 5(5): 3478-3485. http://doi.org/10.15662/IJAREEIE.2016.0505004

[13] Linh, N.T., Dong, D.X. (2013). Optimal location and size of distributed generation in distribution system by artificial bees colony algorithm. An International Journal of Information and Electronics Engineering, 3(1): 63-67. http://doi.org/10.7763/IJIEE.2013.V3.267

[14] El-Zonkoly, A.M. (2011). Optimal placement of multi-distributed generation units including different load models using particle swarm optimization. IET Generation, Transmission, Distribution, 5(7): 760-771. https://doi.org/10.1016/j.swevo.2011.02.003

[15] Samala, R.K., Kotapuri, M.R. (2018). Distributed generation in distribution system for power quality enhancement. International Journal of Engineering & Technology, 7: 167-171. http://doi.org/10.14419/ijet.v7i4.24.21881

[16] Samala, R.K., Kotapuri, M.R. (2020). Optimal allocation of distributed generations using hybrid technique with fuzzy logic controller radial distribution system. SN Applied Sciences, 2: 191. https://doi.org/10.1007/s42452-020-1957-3.

[17] Narayanan, K., Siddiqui, S.A., Fozdar, M. (2015). Identification and reduction of impact of islanding using hybrid method with distributed generation. In proceedings of IEEE Power & Energy Society General Meeting, pp. 1-5. http://doi.org/10.1109/PESGM.2015.7286467

[18] Gomez-Gonzalez, M., Lopez, A., Jurado, F. (2012). Optimization of distributed generation systems using a new discrete PSO and OPF. An International Journal of Electric Power Systems Research, 84: 174-180. http://doi.org/10.1016/j.epsr.2011.11.016

[19] Kotapuri, M.R., Samala, R.K. (2020). Distributed generation allocation in distribution system using particle swarm optimization based ant-lion optimization. International Journal of Control and Automation, 13(1): 414-426. 

[20] Prakash, P., Khatod, D.K. (2016). Optimal sizing and siting techniques for distributed generation in distribution systems: A review. An International Journal of Renewable and Sustainable Energy Reviews, 57: 111-130. http://doi.org/10.1016/j.rser.2015.12.099

[21] Rezaei, F., Esmaeili, S. (2017). Decentralized reactive power control of distributed PV and wind power generation units using an optimized fuzzy-based method. An International Journal of Electrical Power and Energy Systems, 87: 27-42. https://doi.org/10.1016/j.ijepes.2016.10.015

[22] Jamian, J.J., Mustafa, M.W., Mokhlis, H., Baharudin, M.A., Abdilahi, A.M. (2014). Gravitational search algorithm for optimal distributed generation operation in autonomous network. Arabian Journal for Science and Engineering, 39: 7183-7188. http://doi.org/10.1007/s13369-014-1279-0

[23] Rama Prabha, D., Jayabarathi, T., Umamageswari, R., Saranya, S. (2015). Optimal location and sizing of distributed generation unit using intelligent water drop algorithm. An International Journal of Sustainable Energy Technologies and Assessments, 11: 106-113. http://doi.org/10.1016/j.seta.2015.07.003