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Improving Distribution System Performance using the Optimal  

Placement of DGs and Reconfiguration for Different Load Models 

 
Ravi Kant Yadav1 , Dibya Bharti2 , Mala De3 

1 (PhD Scholar), Department of Electrical Engineering, National Institute of Technology, Patna, India, 

raviy.phd19.ee@nitp.ac.in 

2Assistant professor, Department of electrical engineering, Bhagalpur college of engineering, Bhagalpur, Bihar 813210, 

dibya_minu1@rediffmail.com 

3Associate professor, Department of Electrical Engineering, National Institute of Technology, Patna, India, 

mala@nitp.ac.in 

 

 

Article History: 

Received: 12-11-2024 

Revised: 10-12-2024 

Accepted: 15-01-2025 

Abstract: The distribution system often gets significant power losses due to voltage drops. 

To address this issue, various techniques have been proposed in the literature, 

predominantly focusing on single-load models. This study introduces a methodology aimed 

to minimizing system losses and enhancing voltage profiles by integrating distributed 

generation (DG) placement with network reconfiguration (NR) for different load models. 

The analysis considers constant power (CP), constant current (CI), constant impedance 

(CZ), and constant impedance, current and power (ZIP) load models under varying load 

conditions. The methodology employs the index vector approach and a modified whale 

optimization algorithm to identify the optimal placement of DGs. A load escalation rate of 

7% is factored in for planning the distribution system over a five-year. The approach is 

validated using modified 33-bus and 69-bus radial distribution systems (RDS), with 

performance com- pared against genetic algorithm (GA) and particle swarm optimization 

(PSO) techniques for the Constant power load model. The results indicate that combining 

system reconfiguration with DG placement effectively reduces system losses while 

enhancing voltage stability. 

Keywords: Radial distribution system (RDS), Modified whale optimization algorithm 

(MWOA), Constant power load model (CPLM), Constant current load model (CCLM), 

Constant impedance load model (CILM), and Constant impedance, current and power load 

model (ZIPLM). 

 

1 Introduction 

Managing and controlling of distribution networks becomes more complex with fluctuating load 

demand, particularly in areas with high load density. Fixed network topologies, combined with varying 

load demands, often lead to significant power losses in RDS, posing challenges for operators. 

Techniques such as optimal distributed generation placement (ODGP) and network reconfiguration 

(NR) are widely utilized to address these issues. 

NR involves modifying the configuration of feeders by adjusting the operational states of 

sectionalizing and tie switches. DGs are small-scale power generation units, varying from a few kW 

to MW, directly integrated into the current distribution networks. This reconfiguration approach is 

nonlinear in nature and helps alleviate overloaded feeders, thereby reducing system losses. 

mailto:raviy.phd19.ee@nitp.ac.in
mailto:dibya_minu1@rediffmail.com
mailto:mala@nitp.ac.in


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Researchers have explored the use of DGs with different load models, as well as employing series 

voltage regulators and shunt capacitors, to maintain voltage levels and minimize losses in distribution 

systems. However, shunt capacitors and series voltage regulators have limitations. Shunt capacitors 

cannot provide continuously adjustable reactive power, and series voltage regulators operate slowly 

due to their stepwise adjustments. 

Strategically placing DGs with different load models in distribution systems (DS) offers an effective 

solution for improving system performance and addressing the limitations of constant load models, 

shunt capacitors, and series voltage regulators. 

1.1 Concepts and motivations 

Renewable energy has been growing rapidly due to increasing concern for environmental sustainability 

and the wide adaption of EVs in transport sector. As a result, the integration of DGs and EV charging 

stations has become a critical area of research in the field of power systems. The optimal placement of 

DGs and reconfiguration can significantly improve the performance of the distribution system. This 

study aims to address these challenges by proposing a comprehensive framework for the optimal 

integration of DG placement and reconfiguration of DS. In this study, propose the minimization of 

distribution system power losses, improvement of the voltage profile. The different load models taking 

into the optimization model, the placement of DG and reconfiguration can be effectively optimized to 

enhance the overall performance of the distribution system. Moreover, the proposed framework con- 

siders the dynamic nature of load, ensuring that the optimal placement and allocation are robust under 

varying load conditions. Additionally, the varying load profiles are accounted for to ensure that the 

distribution system can accommodate different demand scenarios, leading to improved reliability and 

efficiency [6]. 

1.2 Existing Work: 

Various strategies have been proposed in the literature to address the problem of distribution system 

reconfiguration (DSR) for minimizing power losses. One heuristic method focuses on network recon- 

figuration to achieve loss reduction [1]. Recent advancements include the chaotic stochastic fractal 

search algorithm (CSFSA), which integrates chaotic behavior into the traditional stochastic fractal 

search algorithm (SFSA) to enhance distribution system reconfiguration performance [2]. Several 

approaches have also been introduced for solving the DG allocation problem. For instance, one method 

utilizes the whale optimization algorithm to optimize DG placement and sizing, aiming to reduce 

losses, improve voltage profiles, and enhance reliability while considering residential, commercial, 

and industrial loads [3]. Another study presents a strategy for placing D-STATCOMs with and without 

NR for various load models, including CPLM, CILM, CZLM, and ZIPLM, under load variation 

conditions [4]. The salp swarm algorithm has been applied to concurrently address NR and DG 

allocation, demonstrating effectiveness in minimizing power loss and voltage deviation under different 

scenarios, such as isolated DG placement, isolated NR, and NR post-DG placement [5]. Another 

innovative approach leverages the artificial ecosystem optimizer to integrate DG and capacitor 

allocation for recon- figuring power distribution systems, with practical application demonstrated on 

Egypt's 59-bus Cairo distribution system [6]. Additionally, efficient techniques have been developed 

to simultaneously optimize system reconfiguration and DG placement. These methods aim to 

minimize power loss, improve voltage profiles, balance loads and feeders, and minimize switching 



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operations. For example, an improved moth swarm algorithm has been employed for test systems with 

33 and 84 nodes, achieving multiple objectives effectively [7,8,9]. In [10], the authors discussed 

distribution NR and DG placement for minimizing power losses and optimizing voltage in DS via the 

WOA and evaluated its effectiveness on 33-bus and 69-bus grids. A multi objective approach for NR 

and DG allocation in DS, aiming to minimize losses and operational costs while maximizing stability, 

was presented in [11]. When the TFN is used to model load uncertainty, the multi objective hybrid big 

bang–big crunch (MOHBB-BC) method generates diverse Pareto solutions, considering realistic 

scenarios despite greater losses. The authors of [12] addressed optimal DG allocation, used a hybrid 

gray wolf optimizer that was applied to various DSs, markedly reduced losses, enhanced voltage 

profiles without algorithm tuning, outperformed other methods and identified global optimal solutions. 

Multi objective strategy that integrates NR with DG allocation, and uses evolutionary techniques based 

on Pareto optimality and fuzzy set theory to achieve optimal configurations across multiple criteria 

was introduced in [13]. Paper [14] proposed a simultaneous approach and combined NR and capacitor 

placement in radial distribution networks, employing Johnson's algorithm and an adaptive whale 

optimization algorithm, which is more efficient than the previous methods in the literature. In [15,16], 

the authors discussed a genetic algorithm to optimize DG integration, considering electric vehicles 

(EVs) to reduce power losses and enhance voltage profiles to increase electrical power demand on 

standard 33- and 69-bus systems under various scenarios. the authors explore the advantages of 

modern DG technologies compared to traditional power systems, focusing on economic, 

environmental, and technical aspects. Additionally, it examines various DG technologies that can be 

integrated into power distribution net- works to achieve enhanced loss reduction [17,18]. The impact 

of DG penetration levels on active and reactive power losses, voltage profiles, and node voltage 

deviations across various load types will be analyzed [19,20]. A comprehensive discussion on energy 

and power losses is included [21,22]. The DG units help manage PEV charging impacts on distribution 

systems, along with addressing uncertainties in renewable energy, PEV behavior, and time-varying 

loads, while analyzing reliability through power interruption metrics [23]. Most studies have primarily 

focused on either distributed generation (DG) allocation or NR to improve the performance of 

distribution systems (DS). Only a limited number of researchers have explored the simultaneous 

implementation of NR and DG allocation in distribution networks. A review of the literature reveals 

that many studies model systems using single load models, despite the fact that real-world power 

systems exhibit varying load characteristics. In practical scenarios, loads can be represented as CPLM, 

CILM, CZLM, ZIPLM with load variations. To address these challenges, this study proposes a 

methodology that integrates optimal placement of DGs and reconfiguration in radial distribution 

systems (RDS) while considering different load models. 

1.3 Novelties of Paper 

This paper proposes a novel methodology for optimal placement of DGs and reconfiguration, 

addressing CPLM, CILM, CZLM, and ZIPLM under varying load. This studies have focused on the 

synergies among renewable energy generation, reconfiguration, and distribution system operation. This 

research can maximize the utilization of DG resources, minimize grid congestion, and promote the 

efficient integration of electric vehicles into the transportation and energy sectors. This integrated 

approach contributes to overall system resilience, sustainability, and cost-effectiveness. 

Novel optimization techniques have emerged to address multiple conflicting objectives 



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simultaneously. These objectives may include minimizing real power losses, improving the voltage 

profile, reducing greenhouse gas emissions, and enhancing grid reliability. 

1.4 This Paper's Contributions 

This paper proposes a methodology to minimize system losses and enhance voltage profiles by 

integrating DG placement with subsequent NR for various load models. The approach focuses on 

reducing active and reactive power losses through reconfiguration after optimally placing DGs in RDS 

to improve voltage profiles. The effectiveness of the proposed method is demonstrated using modified 

33- bus and 69-bus RDSs with high DG penetration, considering different load models. Numerical 

results validate the improvements in voltage deviation and the reduction of total power losses achieved 

by this approach. The proposed methodology is compared against existing techniques, such as the GA 

and particle PSO, for different load model scenarios, including modified 33-bus RDSs. 

1.5 Organization of paper 

This paper is structured as, Section 2 outlines the formulation of the problem statement for presented 

approach and methodology, focusing on minimizing voltage deviation and minimizing system losses. 

In Section 3, To evaluate the performance of the formulated approach, it is applied to a modified 33- 

bus RDS and a modified 69-bus RDS under various operational scenarios, incorporating multiple DGs. 

Section 4 presents the results and their analysis for these systems. Section 5 provides a comparative 

evaluation of the proposed approach with existing methods, including particle PSO and the GA, 

specifically for the CP load model on the modified 33-bus RDS. Finally, Section 6 concludes the paper 

by summarizing the key findings. 

2 Problem Formulation 

The proposed methodology aims to minimize active power losses in the DS by combining optimal DG 

placement with NR, considering CPLM, CILM, CZLM, and ZIPLMs. The index vector method is 

utilized to identify the optimal DG location, while the modified whale optimization algorithm 

(MWOA) is applied to determine the optimal DG size. Additionally, the MWOA is employed for NR 

to further enhance system performance. 

In distribution systems, lower voltage levels lead to greater power losses compared to transmission 

systems, primarily due to higher current flows and the comparatively higher resistance of distribution 

lines, and this is computed as  𝑃𝑙𝑜𝑠𝑠 =∑ 𝐼𝑖
2𝑛

𝑖 𝑅𝑖  The aim of this paper is to minimize active power loss. 

Therefore, the objective function for the proposed methodology is as follows: 

             

   𝐹 =  (𝑃𝑙 )  =  𝑀𝑖𝑛 ∑ 𝐼𝑖
2𝑛

𝑖 𝑅𝑖                       (1) 

This paper aims to reduce active power losses, where the current is denoted by 𝐼𝑖, 𝑅𝑖 represents the 

resistance, and n signifies the number of buses in power the system. The constraints are 

                

   Bus Voltage:  0.95 ≤ 𝑉𝑖 ≤ 1.05      

          Power balance: Pg + ∑ Pdg = Pd + Ploss
N
k=1  

Maximum and minimum limits of DG: 50 ≤  𝑃𝑑𝑔  ≤  2500 



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DG type I is assigned to the kW limit, DG type II to the kVAR limit, and DG type III to the kVA 

limit. 

2.2. Electrical Load Modeling:  In the case of static load models, the real and reactive power can be 

expressed using an exponential relationship as follows: 

     𝑃 = 𝑃0(
𝑉

𝑉0
)𝐾𝑝         (2) 

     𝑄 = 𝑄0(
𝑉

𝑉0
)𝐾𝑞        (3) 

At the nominal voltage 𝑉0 of the bus, the active power is denoted as 𝑃0 and the reactive power as 𝑄0. 

The bus load voltage is V, and the load exponents are 𝐾𝑝and 𝐾𝑞 [4]. For the CPLM, 𝐾𝑝= 𝐾𝑞=0. For 

the CILM,𝐾𝑝= 𝐾𝑞=1. For the CZLM, 𝐾𝑝= 𝐾𝑞=2. 

                       𝑃 = 𝑃0[𝛼𝑝(
𝑉

𝑉0
)2] + 𝛽𝑝(

𝑉

𝑉0
) + 𝛾𝑝]                         (4) 

                                 𝑄 = 𝑄0[𝛼𝑞(
𝑉

𝑉0
)2] + 𝛽𝑞(

𝑉

𝑉0
) + 𝛾𝑞]                     (5) 

Where 𝛼𝑝, 𝛽𝑝, 𝛾𝑝, 𝛼𝑞 , 𝛽𝑞 𝑎𝑛𝑑 𝛾𝑞are ZIPLM coefficients. The sum of the coefficients for the ZIPLM for 

both active (P) and reactive (Q) power are normalized to 1.  𝛼𝑝+ 𝛽𝑝+ 𝛾𝑝 = 1, 𝛼𝑞 + 𝛽𝑞+ 𝛾𝑞= 1. For the 

suggested methodology, 𝛼𝑝= 𝛼𝑞= 0.1, 𝛽𝑝= 𝛽𝑞= 0.1, 𝛾𝑝= 𝛾𝑞= 0.8. 𝑃0 represents the real  power 

consumed at a reference voltage 𝑉0  and 𝑄0  represents reactive power consumed at the reference 

voltage. 

2.2.1 Load variation model. - 𝐿𝑜𝑎𝑑𝑡 = 𝐿𝑜𝑎𝑑0 × (1 + 𝑦)                  (6) 

In the 33-bus test system, y represents the annual growth rate, and t denotes the time period during 

which the feeder supports the load, and 𝑦 = 0.07 and 𝑡 = 5 year. 

2.2.2 Vector Indexing Technique [4] - The vector-based indexing technique [3] can be utilized to 

identify the best DG placements. For bus n, the corresponding index vector is defined as 

                                                         𝐼𝑛𝑑𝑒𝑥[𝑛] =
1

𝑉2[𝑛]
+

𝐼𝑞
(𝐾)

𝐼𝑝
(𝑘)

𝑄𝑒𝑓𝑓[𝑛]

 𝑄𝑇𝑜𝑡𝑎𝑙(𝑛) 
            (7) 

In the kth branch, 𝐼𝑞
(𝐾)

 corresponds to the real part of the current, while 𝐼𝑝
(𝑘)

  represents the imaginary 

part. The reactive load and voltage at the nth bus are denoted as 𝑄𝑒𝑓𝑓[𝑛]  and  𝑉[𝑛]  respectively, whereas 

 𝑄𝑇𝑜𝑡𝑎𝑙(𝑛)  signifies the overall reactive load at that bus. 

2.3. MWOA  

The proposed method uses the modified whale optimization algorithm, which is derived from WOA, 

a new metaheuristic method inspired by the cooperative behavior of humpback whales. When tested 

on 29 math problems and 6 structural design challenges, the WOA shows strong performance 

compared with other modern algorithms and conventional approaches [16]. The mathematical 

framework of the WOA encompasses processes such as encircling the prey, employing the bubble-net 



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hunting strategy, and searching for prey. These steps are detailed below: 

2.3.1. Encapsulating prey - In WOA, the target prey denotes the best available solution. Other agents 

strive to enhance their positions to achieve the status of the best search agent. The behavioral model is 

illustrated below: 

                     𝑋⃗ (𝑡+1) =𝑋⃗ ∗(𝑡)−𝑈⃗ . 𝑍⃗                               (8) 

                      𝑍⃗  = [𝑊. 𝑋⃗∗𝑡− 𝑋⃗. 𝑡]     (9) 

                     𝑈⃗  = 2. 𝑎 . 𝑟⃗  − 𝑎                                                     (10) 

                          𝑊  = 2. 𝑟⃗                             (11) 

Where 𝑋⃗ ∗ represents the best solution, X denotes the position vector, Z is a random variable 

influencing the update, t indicates the current iteration, 𝑈⃗ and 𝑊  are coefficient vectors, 𝑎  is a 

diminishing factor with values ranging from 2 to 0, and 𝑟⃗  is a randomly selected vector within the 

range [0, 1] 

2.3.2. Bubble net foraging method – This paper presents two methods for hunting. 

a. Contracting encircling of the prey: Here, a is a vector within the range [-a, a], with the value of 

‘a’ decreasing from 2 to 0. In this case, the position U is randomly initialized within the interval [-1, 

1]. The new position of ‘a’, which is bounded by 0 ≤ a ≤ 1, is determined between the initial location 

and the current optimal agent's position in a 2D space, as shown in Equation 8, moving from (X, Y) to 

(X∗,Y∗) 

b. Spiral movement updating - The spiral equation is employed to model the helical movement. 

           X  (t + 1) = Z . 𝑒𝑏𝑙. cos( 2πl) + X ∗                 (12) 

Were as  𝑒𝑏𝑙 suggests an exponential component, where b and l are parameters and cos( 2πl)introduces 

an oscillatory (periodic) influence. During hunting, whales approaching their prey from two separate 

directions simultaneously. For the next two methods used to update the positions of the whales, having 

a 50% probability is applied to determine which approach will be used. 

 X (t + 1) = {
X ∗ (t)  −  U  . Z                         if p <  0.5

Z ′. 𝑒𝑏𝑙. cos( 2πl)  +  X ∗       if p ≥  0.5
                (13) 

c. Search for prey - Instead of using the most efficient search agent, the global optimal values are 

revised through a randomly selected search agent. 

 𝑍⃗  = [𝑊 . 𝑋⃗ 𝑟⃗ 𝑎 𝑛 𝑑  − 𝑋⃗ ]                           (14) 

 X(t+1)=𝑋⃗ 𝑟⃗ 𝑎 𝑛 𝑑 −U Z                               (15) 

 𝑋⃗ 𝑟⃗ 𝑎 𝑛 𝑑 is the current iteration's random value. 

2.4. DG Placement Optimization: An optimization algorithm is required to determine the optimal locations and 

capacities of DGs in the DS and TPL and TQL represents total active power and total reactive power. 



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Fig.1. Flow chart of proposed DGs placement. 

2.5. Distribution system reconfiguration: MWOA is utilized for NR to reduce system losses and improve 

the voltage profile. A flowchart depicting the proposed methodology is shown in Fig. 2. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                                             

 

 

 

 

 

 

 

Fig.2. Algorithm Flow for Reconfiguration. 



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3 Implementation of the proposed technique 

To apply the proposed method, two modified radial distribution systems are considered: 33-bus and a 

69-bus, both integrated with multiple DGs units. In the 33-bus system, three solar generation units of 

10 kW, 20 kW, and 35 kW are added at buses 6, 21, and 26, respectively, as illustrated in Fig. 3a. 

Similarly, in the 69-bus system, six solar generation units with capacities of 25.0 kW, 30.0 kW, 20.0 

kW, 35.0 kW, 40.0 kW, and 45.0 kW are connected at buses 33, 69, 62, 65, 44, and 46, as shown in 

Fig. 3b. 
 

Fig. 3a. Modified 33-bus RDS 

Table.1. Load Data and Line Data of modified 33 bus test system 

Load data specifies the active (P) and reactive (Q) power at each bus and Line data specifies the 

connection between buses and the impedance (R, X). 
 

S. No. From Bus To Bus Load (P) 

(kW) 

Load (Q) 

(kVAR) 

R (p.u.) X (p.u.) 

1 1 2 100 60 0.0922 0.0470 

2 2 3 90 40 0.4930 0.2511 

3 3 4 120 80 0.3660 0.1864 

4 4 5 60 30 0.3811 0.1941 

5 5 6 60 20 0.8190 0.7070 

6 6 7 200 100 0.1872 0.6188 

7 7 8 200 100 0.7114 0.2351 

8 8 9 60 20 1.0300 0.7400 

9 9 10 60 20 1.0440 0.7400 

10 10 11 45 30 0.1966 0.0650 

11 11 12 60 35 0.3744 0.1238 

12 12 13 60 35 1.4680 1.1550 

13 13 14 120 80 0.5416 0.7129 

14 14 15 60 10 0.5910 0.5260 

15 15 16 60 20 0.7463 0.5450 

16 16 17 60 20 1.2890 1.7210 

17 17 18 90 40 0.7320 0.5740 



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18 18 19 90 40 0.1640 0.1565 

19 19 20 90 40 1.5042 1.3554 

20 20 21 90 40 0.4095 0.4784 

21 21 22 90 40 0.7089 0.9373 

22 22 23 90 50 0.4512 0.3083 

23 23 24 420 200 0.8980 0.7091 

24 24 25 420 200 0.8960 0.7011 

25 25 26 60 25 0.2030 0.1034 

26 26 27 60 25 0.2842 0.1447 

27 27 28 60 25 1.0590 0.9337 

28 28 29 120 70 0.8042 0.7006 

29 29 30 200 600 0.5075 0.2585 

30 30 31 150 70 0.9744 0.9630 

31 31 32 210 100 0.3105 0.3619 

32 32 33 60 40 0.3410 0.5302 

33 8 21 0 0 2.0000 2.0000 

34 9 15 0 0 2.0000 2.0000 

35 12 22 0 0 2.0000 2.0000 

36 18 33 0 0 0.5000 0.5000 

37 25 29 0 0 0.5000 0.5000 

 

Fig. 3b. Modified 69-bus RDS 

In the analyzed system, centralized power generation system is located at bus 1. The total power demand 

is 3650 kW for the modified 33-bus system and 3771 kW for the modified 69-bus system. 



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Table.2. Load Data and Line Data of modified 69 bus test system 

Load data specifies the active (P) and reactive (Q) power at each bus and Line data specifies the 

connection between buses and the impedance (R, X). 
 

S. No. From Bus To Bus Load (P) 

(kW) 

Load (Q) 

(kVAR) 

R (p.u.) X (p.u.) 

1 1 2 0 0 0.0005 0.0012 

2 2 3 0 0 0.0005 0.0012 

3 3 4 0 0 0.0015 0.0036 

4 4 5 0 0 0.0251 0.0294 

5 5 6 2.6 2.2 0.366 0.1864 

6 6 7 40.4 30 0.3811 0.1941 

7 7 8 75 54 0.0922 0.047 

8 8 9 30 22 0.0493 0.0251 

9 9 10 28 19 0.819 0.2707 

10 10 11 145 104 0.1872 0.0619 

11 11 12 145 104 0.7114 0.2351 

12 12 13 8 5 1.03 0.34 

13 13 14 8 5.5 1.044 0.345 

14 14 15 8 5.5 1.058 0.3496 

15 15 16 45.5 30 0.1966 0.065 

16 16 17 60 35 0.3744 0.1238 

17 17 18 60 35 0.0047 0.0016 

18 18 19 0 0 0.3276 0.1083 

19 19 20 1 0.6 0.2106 0.069 

20 20 21 114 81 0.3416 0.1129 

21 21 22 5 3.5 0.014 0.0046 

22 22 23 0 0 0.1591 0.0526 

23 23 24 28 20 0.3463 0.1145 

24 24 25 0 0 0.7488 0.2475 

25 25 26 14 10 0.3089 0.1021 

26 26 27 14 10 0.1732 0.0572 

27 27 28 26 18.6 0.0044 0.0108 

28 28 29 26 18.6 0.064 0.1565 

29 29 30 0 0 0.3978 0.1315 

30 30 31 0 0 0.0702 0.0232 

31 31 32 0 0 0.351 0.116 

32 32 33 14 10 0.839 0.2816 

33 33 34 19.5 14 1.708 0.5646 

34 34 35 6 4 1.474 0.4873 

35 35 36 26 18.55 0.0044 0.0108 



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36 36 37 26 18.55 0.064 0.1565 

37 37 38 0 0 0.1053 0.123 

38 38 39 24 17 0.0304 0.0355 

39 39 40 24 17 0.0018 0.0021 

40 40 41 1.2 1 0.7283 0.8509 

41 41 42 0 0 0.31 0.3623 

42 42 43 6 4.3 0.041 0.0478 

43 43 44 0 0 0.0092 0.0116 

44 44 45 39.22 26.3 0.1089 0.1373 

45 45 46 39.22 26.3 0.0009 0.0012 

46 46 47 0 0 0.0034 0.0084 

47 47 48 79 56.4 0.0851 0.2083 

48 48 49 384.7 274.5 0.2898 0.7091 

49 49 50 384.7 274.5 0.0822 0.2011 

50 50 51 40.5 28.3 0.0928 0.0473 

51 51 52 3.6 2.7 0.3319 0.1114 

52 52 53 4.35 3.5 0.174 0.0886 

53 53 54 26.4 19 0.203 0.1034 

54 54 55 24 17.2 0.2842 0.1447 

55 55 56 0 0 0.2813 0.1433 

56 56 57 0 0 1.59 0.5337 

57 57 58 0 0 0.7837 0.263 

58 58 59 100 72 0.3042 0.1006 

59 59 60 0 0 0.3861 0.1172 

60 60 61 1244 888 0.5075 0.2585 

61 61 62 32 23 0.0974 0.0496 

62 62 63 0 0 0.145 0.0738 

63 63 64 227 162 0.7105 0.3619 

64 64 65 59 42 1.041 0.5302 

65 65 66 18 13 0.2012 0.0611 

66 66 67 18 13 0.0047 0.0014 

67 67 68 28 20 0.7394 0.2444 

68 68 69 28 20 0.0047 0.0016 

69 11 43 0 0 0.5 0.5 

70 13 20 0 0 0.5 0.5 

 

 

 

 

 

 

 



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4 Results and Discussion 

The analysis was performed using MATLAB version 9.0 on a Windows 10 system (Intel® Core™ i3 

Processor, 3.30 GHz, 4 GB RAM). The results were obtained for DG placement, both with and without 

network reconfiguration, in the modified 33-bus and 69-bus RDS under various load models (CPLM, 

CILM, CZLM, and ZIPLM). The system's performance, including voltage profiles, power loss, and 

other metrics, was evaluated across different load models. The following three scenarios were 

considered for the analysis: 

 Case 1: Only NR of the RDS. 

 Case 2: Only DGs placement of the RDS. 

 Case 3: NR after placement of DGs in the RDS. 

4.1 CPLM Analysis for the Modified 33-bus RDS 

The CPLM for both cases is provided in Table 3. The analysis shows a significant reduction in line 

loss, which decreases from 198.7791 kW to 23.4077 kW in Case 2. In Case 3, the line loss is further 

reduced to 19.6848 kW. This reduction in active power loss is achieved by installing a total of six DGs 

units at the following bus locations and capacities: bus 25 (372 kW), bus 28 (459 kW), bus 32 (500 

kW), bus 33 (500 kW), bus 11 (391 kW), and bus 17 (500 kW). Fig. 4 illustrates the improvement in 

voltage profile in Case 3 compared to Case 2. It also highlights, a slightly higher voltage deviation is 

observed between buses 17 and 18. 

 
 

 

 

 

 

 

 

 

 

 



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                                                      Fig. 4. Voltage variation for the CPLM  

 

                    

 

 

 

 

 

 

 

Fig. 5. Voltage variation for the CILM 

 



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4.2 CILM Analysis for the Modified 33-bus RDS 

Table 4 presents the results for the CILM, comparing both cases. The analysis reveals a reduction in 

line loss from 171.6116 kW to 31.5958 kW in Case 2, and further to 28.1628 kW in Case 3. This 

reduction in active power loss is achieved by placing six DGs units at the following bus locations and 

capacities: bus 17 (253 kW), bus 21 (176 kW), bus 25 (465 kW), bus 29 (436 kW), bus 31 (292 kW), 

and bus 8 (328 kW). Fig. 5 illustrates that the voltage deviation in Case 3 closely follows the trend 

observed in Case 2.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

        

                                                   Fig. 6. Voltage profile for CZLM                          



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Fig. 7. Voltage profile for ZIPLM 

 

4.3 CZLM Analysis for the Modified 33-bus RDS 

Table 5 compares the results for the CZLM in both cases. The findings show that line loss decreases 

from 151.8268 kW to 22.3601 kW in Case 2, and further to 29.2304 kW in Case 3. This reduction in 

real power loss is achieved by installing six DGs units at the following bus locations and capacities: 

bus 15 (340 kW), bus 27 (328 kW), bus 29 (287 kW), bus 33 (500 kW), bus 3 (500 kW), and bus 8 

(260 kW). Fig. 6 demonstrates that the voltage deviation in Case 3 is lower than in Case 2, indicating 

that reconfiguration is unnecessary after DG placement. 

4.4 ZIPLM Analysis for the Modified 33-bus RDS 

Table 6 presents the results for the ZIPLM in both cases. The analysis reveals that line loss decreases 

from 191.6269 kW to 30.817 kW in Case 2, and further reduces to 26.6369 kW in Case 3. This reduction 

in real power loss is achieved by installing six DGs units at the following bus locations and capacities: 

bus 5 (270 kW), bus 14 (422 kW), bus 16 (365 kW), bus 29 (500 kW), bus 33 (445 kW), and bus 3 

(183 kW). Fig. 7 illustrates that the voltage deviation in Case 3 is improved compared to Case 

2. The voltage deviation in Case 3 is improved compared to Case 2, with execution times of 2285.256 

seconds for Case 3 and 3.2195 seconds for Case 2. 

 

 

 

 

 

 

 

 

 

 



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4.5 CPLM Analysis for the Modified 69-bus RDS 

Table 7 presents the results for the CPLM in both cases. The analysis shows that line loss decreases 

from 214.2445 kW to 22.2445 kW in Case 2, and further reduces to 15.427 kW in Case 3. This reduction 

in real power loss is achieved by installing six DGs units at the following bus locations and capacities: 

bus 2 (500 kW), bus 63 (500 kW), bus 68 (500 kW), bus 59 (500 kW), bus 61 (500 kW), and bus 11 

(500 kW). Fig. 8 illustrates that the voltage profile is significantly lower between buses 11 and 28, and 

between buses 50 and 64. The voltage deviation in Case 3 is improved compared to Case 2, with 

execution times of 2285.256 seconds for Case 3 and 3.2195 seconds for Case 2. 

 

 

 

 

 

 

 

 

 

 

 

 

Fig. 8. Voltage profile for CPLM 



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                              Fig. 9. Voltage profile for CILM 

4.6 CILM Analysis for the Modified 69-bus RDS 

Table 8 compares the results for the CILM in both cases. The analysis reveals that line loss decreases 

from 180.67 kW to 11.340 kW in Case 2, and further reduces to 10.960 kW in Case 3. This reduction 

in real power loss is achieved by installing six DGs units at the following bus locations and capacities: 

bus 59 (500 kW), bus 62 (500 kW), bus 63 (500 kW), bus 65 (226 kW), bus 69 (173 kW), and bus 19 

(500 kW). Fig. 9 illustrates that the voltage deviation in Case 3 follows the trend observed in Case 2. 

The execution time for Case 2 is 3.2498 seconds, while for Case 3, it is 2078.8749 seconds. 

4.7 CZLM Analysis for the Modified 69-bus RDS 
 

Table 9 compares the results for the CZLM in both cases. The analysis shows that line loss decreases 

from 152.87 kW to 8.885 kW in Case 2, and further reduces to 12.322 kW in Case 3. This reduction 

in real power loss is achieved by installing six DGs units at the following bus locations and capacities: 

bus 50 (500 kW), bus 59 (499 kW), bus 64 (500 kW), bus 69 (500 kW), bus 61 (500 kW), and bus 22 

(210 kW). Fig. 10 illustrates that the voltage deviation in Case 3 is lower than that in Case 2, indicating 

that reconfiguration is unnecessary after DG placement. Time taken to execute the result for base_case, 

case2 and case3 is 0.49343 sec, 3.1866 sec and 2298.8618 sec respectively that shows the execution 

time increasing from base_case to case 3. 



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                                 Fig. 10. Voltage deviations for CZLM 

 

     

                                  Fig. 11. Voltage deviation for ZIPLM 

 

4.8 ZIPLM Analysis for the Modified 69-bus RDS 

Table 10 presents the results for the ZIP load model in both cases. The analysis reveals that line loss 

decreases from 204.078 kW to 18.1847 kW in Case 2, and further reduces to 16.732 kW in Case 3. 

This reduction in real power loss is achieved by installing six DGs units at the following bus locations 

and capacities: bus 62 (468 kW), bus 63 (500 kW), bus 65 (500 kW), bus 69 (448 kW), bus 44 (243 

kW), and bus 18 (496 kW). Fig. 11 illustrates that the voltage deviation in Case 3 is improved com- 

pared to Case 2. The execution time for Case 2 is 3.2975 seconds, while for Case 3, it is 2321.9955 

seconds. 



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5 Comparison of the proposed methodology with existing methodologies 

To compare the performance of the proposed technique with existing techniques, PSO and GA are 

applied to the modified 33-bus RDS using the CPLM for Cases 2 and 3. The results of this comparison 

are presented in Table 11, and the graphical representation is shown in Fig. 14. In PSO_Case 2, The 

decrease in active power dissipation and reactive power requirements are 47.990 kW and 41.23 kVAR, 

respectively, achieved by placing two DGs at bus 25 (822 kW) and bus 7 (1000 kW). In GA_Case 2, 

the savings are 47.0975 kW for real power and 36.0481 kVAR for reactive power, which result from 

the placement of two DGs at buses 33 (72 kW) and 10 (722 kW). For MWOA_Case 2 with the CPLM, 

real and reactive power loss savings of 121.3258 kW and 81.7193 kVAR are achieved, with DGs placed 

at buses 33 (500 kW and 242.161 kVAR), and at bus 15 (500 kW and 242.161 kVAR). Using the CILM 

in MWOA_Case 2, savings of 127.726 kW in active power and 86.426 kVAR in reactive power, placing 

DGs strategically at buses 16 (456 kW and 220.851 kVAR), and at buses 31 (434 kW and 210.196 

kVAR). 



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The active and reactive power loss reductions of 132.84 kW and 88.88 kVAR, respectively, are 

achieved in MWOA_Case 2 with the CZLM. This is accomplished by placing two DGs at buses 17 

(463 kW and 224.2411 kVAR) and 33 (375 kW and 181.6208 kVAR). For the ZIPLM in 

MWOA_Case 2, the savings in real power loss increase to 120.374 kW, while reactive power savings 

rise to 80.414 kVAR. These savings are realized by placing two DGs at buses 33 (500 kW and 242.160 

kVAR) and 18 (460 kW and 222.780 kVAR). Fig. 12 illustrates the voltage profile for Case 2 across 

different load models. The performance evaluation of the proposed technique compared to existing 

techniques (PSO_Case 3 and GA_Case 3) for the CPLM on the modified 33-bus RDS is summarized 



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in Table 11. 

 

Fig. 12. Voltage profile of case 2 for different load model 

The results show that in PSO_Case 3, the real power loss is reduced by 78.13 kW and the reactive power 

by 52.232 kVAR. In GA_Case 3, the reductions are 84.4797 kW for real power loss and 60.0733 

kVAR for reactive power. Using WOA_Case 3 for the CPLM, the savings increase to 121.3258 kW 

for real power loss and 135.855 kW. with savings in reactive power rising from 81.7193 kVAR to 

87.4698 kVAR. For MWOA_Case 3 with the CILM, the active power loss savings increase from 

127.726 kW to 131.897 kW, while the reactive power savings decrease slightly from 86.426 kVAR to 

83.45 kVAR. In MWOA_Case 3 for the CZLM, real power loss decreases from 132.8463 kW to 

127.6907 kW, with reactive power savings reduced from 88.8863 kVAR to 80.0506 kVAR. Finally, 

with the ZIPLM in MWOA_Case 3, the active power loss savings increase from 120.374 kW to 

135.569 kW, while the reactive power savings improve from 80.414 kVAR to 84.820 kVAR. Fig. 13 

displays the voltage profile for Case 3 across the various load models. 
 

Fig. 13. Voltage deviation of case3 for different load model 



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Fig. 14. Comparison of proposed methodology with existing methodologies in terms of active 

power saving (kW) and reactive power saving (kVAR) for modified 33 bus system 

6 Conclusions 

In this paper, a methodology for NR after DG placement has been proposed for CP, CI, and CZ load 

models, along with a ZIP load model considering load variation. The analysis demonstrates that 

reconfiguration post-DG placement significantly enhances voltage profiles, reduces line losses, and 

leads to power savings. The findings show that, for the CP and CI load models, the voltage deviation 

and performance improve after reconfiguration. However, for the CZ load model, reconfiguration after 

DG placement does not provide additional benefits, indicating that it may not be necessary. The ZIP 

load model analysis also confirms that the voltage profiles and power savings improve with 

reconfiguration. Overall, this methodology proves to be an efficient strategy for optimizing power 

distribution systems, except for the CZ load model, where reconfiguration is not required after DG 

placement. 

 

 

 

 



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