Optimal DG placement for benefit maximization in distribution networks by using Dragonfly algorithm
 M. C. V. Suresh^{1}Email author and
 Edward J. Belwin^{2}
https://doi.org/10.1186/s4080701800507
© The Author(s) 2018
Received: 29 December 2017
Accepted: 30 April 2018
Published: 30 May 2018
Abstract
Distributed generation (DG) is small generating plants which are connected to consumers in distribution systems to improve the voltage profile, voltage regulation, stability, reduction in power losses and economic benefits. The above benefits can be achieved by optimal placement of DGs. A novel natureinspired algorithm called Dragonfly algorithm is used to determine the optimal DG units size in this paper. It has been developed based on the peculiar behavior of dragonflies in nature. This algorithm mainly focused on the dragonflies how they look for food or away from enemies. The proposed algorithm is tested on IEEE 15, 33 and 69 test systems. The results obtained by the proposed algorithm are compared with other evolutionary algorithms. When compared with other algorithms the Dragonfly algorithm gives best results. Best results are obtained from type III DG unit operating at 0.9 pf.
Keywords
Introduction
Interconnection of generating, transmitting and distribution systems usually called as electric power system. Usually distribution systems are radial in nature and power flow is unidirectional. Due to ever growing demand modern distribution networks are facing several problems. With the installation of different distributed power sources like distributed generations, capacitor banks etc, several techniques have been proposed in the literature for the placement of DGs. Most of the losses about 70% losses are occurring at distribution level which includes primary and secondary distribution system, while 30% losses occurred in transmission level. Therefore, distribution systems are main concern nowadays. The losses targeted at distribution level are about 7.5%.

Reduced system losses

Voltage profile improvement

Frequency improvement

Reduced emissions of pollutants

Increased overall energy efficiency

Enhanced system reliability and security

Improved power quality

Relieved Transmission & Distribution congestion

Deferred investments for upgrades of facilities

Reduced fuel costs due to increased overall efficiency

Reduced reserve requirements and the associated costs

Increased security for critical loads.
AbuMouti and ElHawary (2010) proposed ABC to find the optimal allocation and sizing of distributed generation. Distributed generation uncertainties (Zangiabadi et al. 2011) have been taken in account for the placement of DG.
Alonso et al. (2012), Rahim et al. (2012), DoagouMojarrad et al. (2013) and Hosseini et al. (2013) proposed evolutionary algorithms for the placement of distributed generation. Nekooei et al. (2013) proposed Harmony Search algorithm with multiobjective placement of DGs.
A novel combined hybrid method GA/PSO is presented in Moradi and Abedini (2011) for DG placement. With unappropriated DG placement, can increase the system losses with lower voltage profile. The proper size of DG gives the positive benefits in the distribution systems. Voltage profile improvement, loss reduction, distribution capacity increase and reliability improvements are some of the benefits of system with DG placement (Rahim et al. 2013; Ameli et al. 2014).
Embedded Meta EvolutionaryFirefly Algorithm (EMEFA) was proposed in Rahim et al. (2013) for DG allocation. Here how losses are varied with population size are considered. Simultaneous placement of DGs and capacitors with reconfiguration was proposed by Esmaeilian and Fadaeinedjad (2015) and Golshannavaz (2014). Dynamic load conditions have been taken in Gampa and Das (2015). Big bang big crunch method was implemented for the placement of DG in Hegazy et al. (2014). Murty and Kumar (2014) uses mesh distribution system analysis for the placement of distributed generation with time varying load model. Probabilistic approach with DG penetration was discussed in Kolenc et al. (2015). The backtracking search optimization algorithm (BSOA) was used in DS planning with multitype DGs in ElFergany (2015), BSOA was proposed for DG placement with various load models.
Reddy et al. (2017a) and Reddy et al. (2017b) proposed whale optimization and Ant Lion optimization algorithm for sizing of DGs. In most of the studies economic analysis has not been taken.
A novel natureinspired algorithm called Dragonfly algorithm is used to find the optimal DG size in this paper. The optimal size of DGs at different power factors are determined by DA algorithm to reduce the power losses in the distribution system as much as possible and enhancing the voltage profile of the system. The economic analysis of DG placement is also considered in this paper.
Problem formulation
Objective function
Constraints

Voltage constraints$$\begin{aligned} 0.95 \le V_{i} \le 1.05 \end{aligned}$$(2)

Power balance constraints$$\begin{aligned} {P} + \sum \limits _{k = 1}^{{N}} {{P_{DG}} = {P_d} + {P_{loss}}} \end{aligned}$$(3)

Upper and lower limits of DG$$\begin{aligned} 60 \le {P_{DG}} \le 3500 \end{aligned}$$(4)
Loss sensitivity factors method
Identification of optimal locations using loss sensitivity factors
Algorithm
 Step 1 :

Read line and load data of the system and solve the feeder line flow for the system using the branch current load flow method.
 Step 3 :

Find the loss sensitivity factors using Eq. (7).
 Step 4 :

Store the buses with loss sensitivity factors arranged in decreasing order in a vector according to their positions.
 Step 5 :

Normalize the magnitudes of the voltages for all the buses using Eq. (9).
 Step 6 :

Select the buses with normalized voltage magnitudes less than 1.01 as the best suitable locations for DG placement.
LSF method is applied to 15bus, 33bus and 69bus IEEE systems and the locations are given in the tables below (Tables 1, 2, 3).
Loss sensitivity factors for 15bus system
\(\frac{\partial \mathbf{PL }}{\partial \mathbf{Q }}\) (decreasing)  Bus number  VNorm[i]  Base voltage V[i] 

2966.2  2  1.0224  0.9713 
1643.7  6  1.0087  0.9582 
1548.5  3  1.0070  0.9567 
852.6  11  1.0000  0.9500 
618.2  4  1.0010  0.9509 
526.6  12  0.9956  0.9458 
413.4  9  1.0189  0.9680 
314.1  15  0.9984  0.9484 
292.6  14  0.9985  0.9486 
281.1  7  1.0063  0.9560 
167.8  13  0.9942  0.9445 
161.3  8  1.0073  0.9570 
134.2  10  1.0178  0.9669 
125.6  5  0.9999  0.9499 
From above table first best location for DG placement is 6.
Loss sensitivity factors for 33bus system
\(\frac{\partial \mathbf{PL }}{\partial \mathbf{Q }}\) (decreasing)  Bus number  VNorm[i]  Base voltage V[i] 

1678  6  0.9995  0.9495 
1365  28  0.9827  0.9335 
1325  3  1.035  0.9829 
From above table first best location for DG placement is 6.
Loss sensitivity factors for 69bus system
\(\frac{\partial \mathbf{PL }}{\partial \mathbf{Q }}\) (decreasing)  Bus number  VNorm[i]  Base voltage V[i] 

2664.8  57  0.9896  0.9401 
1344.9  58  0.9779  0.9290 
935.7  7  1.0324  0.9808 
882.9  6  1.0422  0.9901 
848.3  61  0.9604  0.9123 
635.0  60  0.9681  0.9197 
571.8  10  1.0236  0.9724 
526.9  59  0.9734  0.9248 
456.8  55  1.0178  0.9669 
449.7  56  1.0132  0.9626 
From above table first best location for DG placement is 61.
The Dragonfly algorithm (DA)
The DA algorithm was proposed by Mirjalili (2015). It has been developed based on swarm intelligence and the peculiar behavior of dragonflies in nature. This algorithm mainly focused on the dragonflies how they look for food or away from enemies.
The static behavior of dragonflies, i.e, looking for food can be treated as exploitation phase and evade from enemies can be treated as exploration phase. The static swarm dragonflies consist of small group of dragonflies which are hunting the preys in small space. The direction and velocity of this dragonflies are small and abrupt changes will be there in the direction. Dynamic swarm with constant direction and more number of different dragonflies moves to another place over a long distance.
The mathematical model of DA algorithm can be modeled with the following five behaviors of dragonflies.

Position of current dragonflies is represented by X

\(X_{k}\) represents position of \( k^{th} \)neighboring dragonflies

N is the total number of neighboring dragonflies
Implementation of DA
 Step 1 :

Feeder line flow is solved by branch current load flow method.
 Step 2 :

Find the best DG locations using the index vector method.
 Step 3 :

Initialize the population/solutions and itmax = 100, Number of DG locations d=1,\(dg_{min}=60,dg_{max}=3500\).
 Step 4 :

Generate the population of DG sizes randomly using equation
\(population = (dg_{max}  dg_{min}) \times rand() + dg_{min}\)
where \(dg_{min}\) and \(dg_{max}\) are minimum and maximum limits of DG sizes.
 Step 5 :

Determine active power loss for generated population by performing load flow.
 Step 6 :

Select low loss DG as current best solution.
 Step 8 :

Determine the losses for updated population by performing load flow.
 Step 9 :

Replace the current best solution with the updated values if obtained losses are less than the current best solution. Otherwise go back to step 7
 Step 10 :

If maximum number of iterations is reached then print the results.
Results and discussion
DA algorithm in the application of DG planning problem to obtain DG size and economic analysis is presented in this section. IEEE 15, 33 and 69 bus test systems are evaluated using MATLAB.
Economic analysis
The mathematical model is given below for cost calculations.
Cost of energy losses (CL)
Cost component of DG for real and reactive power
IEEE 15bus system
Table 4 shows the real,reactive power losses and minimum voltages after the placement of different types of DGs. The optimal location for 15 bus test system is 6. The minimum voltage is more in case of type III DG operating at 0.9 pf. The losses are also lower with DG type III operating at 0.9 pf when compared to DG operating at upf in Table 4. This is due to both real and reactive powers are supplied by the DG at lagging pf. Reactive power is not supplied by type III DG when operating at Unity pf. Hence, losses are higher when compared to DG operating at 0.9 pf lagging.
Results for 15 bus system
With out DG  With DG at 0.9pf  With DG at UPF  

DG location  –  6  6 
DG size (kVA)  –  907.785  675.248 
TLP (kW)  61.7933  33.385  45.8035 
TLR (kVAR)  57.2969  29.89  41.88 
Vmin (p.u.)  0.9445  0.959  0.9527 
Cost of Energy losses ($)  4970.3  2685.31  3684.18 
Cost of PDG ($/MW h)  –  16.5404  13.754 
Cost of QDG ($/MVAR h)  –  1.8656  – 
Results for 33 bus
Without installation of DG, real and reactive power losses are 211 kW and 143 kVAr, respectively. With installation of DG at unity pf, real, reactive power losses are 111.0338 kW and 81.6859 kVAr, respectively. With DG at 0.9 pf lag, real, reactive power losses are 70.8652 kW and 56.7703 kVAr, respectively.
The losses obtained are lower when lagging power factor DG is used when compared to unity power factor DG. This is due to reactive power available in lagging power factor DG.
Results for 33 bus system with DG at upf
Without DG  Voltage sensitivity index method (Murthy and Kumar 2013)  Proposed method  

DG location  –  16  6 
DG size (kW)  –  1000  2590.2 
Total real power loss (TLP) (kW)  211  136.7533  111.0338 
Total reactive power (TLR)loss (kVAR)  143  92.6599  81.6859 
Vmin (p.u.)  0.904  0.9318  0.9424 
Cost of energy losses ($)  16,982.5724  11,007.9901  8930.65 
Cost of PDG ($/MW h)  20.25  52.05 
Results for 33 bus system with DG at 0.9 pf
With DG  

Voltage sensitivity index method (Murthy and Kumar 2013)  Proposed method  
DG location  16  6 
DG size (kVA)  1200  3073.5 
TLP (kW)  112.7864  70.8652 
TLR (kVAR)  77.449  56.7703 
Vmin (p.u.)  0.9378  0.9566 
Cost of Energy losses ($)  9078.7686  5700.01 
Cost of PDG ($/MW h)  21.85  55.5 
Cost of QDG ($/MVAR h)  2.1207  6.2 
Results for 69 bus
Without DG real, reactive power losses are 225 kW and 102.1091 kVAr, respectively. With the installation of DG at unity pf, the real and reactive power losses are 83.2261 kW and 40.5754 kVAr, respectively. With DG at 0.9 pf lag real, reactive power losses are 27.9636 kW and 16.4979 kVAr.
The losses obtained are lower when lagging power factor DG is used when compared to unity power factor DG. This is due to reactive power available in lagging power factor DG.
Cost of energy losses, cost of PDG and cost of QDG are also shown in Tables 5 and 6. From table the cost of energy losses is reduced from 18,101.7 $ to 2249.2 $ when DG is operating at 0.9pf lag and it reduced to 6694 $ when operating at unity pf. Cost of energy losses are less when DG is operating at 0.9pf.
Results for 69 bus system with DG at upf
Without DG  Voltage sensitivity index method (Murthy and Kumar 2013)  Proposed method  

DG location  65  61  
DG size (kW)  1450  1872.7  
TLP (kW)  225  112.0217  83.22 
TLR (kVAR)  102.1091  55.1172  40.57 
Vmin (p.u.)  0.909253  0.9660621  0.9685 
Cost of energy losses ($)  18,101.7621  9017.2139  6694 
Cost of Pdg ($/MW h)  –  29.25  37.7 
Results for 69 bus system with DG at 0.9 pf
With DG  

Voltage sensitivity index method (Murthy and Kumar 2013)  Proposed method  
DG location  65  61 
DG size (kVA)  1750  2217.3 
TLP (kW)  65.4502  27.9636 
TLR (kVAR)  35.625  16.4979 
Vmin (p.u.)  0.969302  0.9728 
Cost of Energy losses ($)  5268.4297  2249.2 
Cost of PDG ($/MW h)  31.75  40.1 
Cost of QDG ($/MVAR h)  3.083  4.48 
Conclusions
A novel natureinspired algorithm called Dragonfly algorithm is used to determine the optimal DG units size in this paper.It has been developed based on the peculiar behavior of dragonflies how they look for food or away from enemies. Reduction in system real power losses with low cost are chosen as objectives in this paper. This proposed optimization technique has been applied on typical IEEE 15, 33 and 69 bus radial distribution systems with different two types of DGs and compared with other algorithms. Better results have been achieved with combination of loss sensitivity factor method and DA algorithm when compared with other algorithms. Best results are obtained from type III DG operating at 0.9 pf.
Declarations
Authors' contributions
MCVS: carried out the literature survey, participated in DG location section. MCVS and EJB: participated in study on different natureinspired algorithm for DG sizing. MCVS: carried out the DG sizing algorithm design and mathematical modeling. MCVS and EJB: participated in the assessment of the study and performed the analysis. MCVS and EJB: participated in the sequence alignment and drafted the manuscript. Both authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
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Authors’ Affiliations
References
 AbuMouti, F. S., & ElHawary, M. E. (2010). Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm. IEEE Transactions on Power Delivery, 26(4), 2090–2101.View ArticleGoogle Scholar
 Acharya, N., Mahat, P., & Mithulananthan, N. (2006). An analytical approach for dg allocation in primary distribution network. International Journal of Electrical Power & Energy Systems, 28(10), 669–678.View ArticleGoogle Scholar
 Ackermann, T., Andersson, G., & Soder, L. (2001). Distributed generation: A definition. Electric Power Systems Research, 57(3), 195–204.View ArticleGoogle Scholar
 Alonso, M., Amaris, H., & AlvarezOrtega, C. (2012). Integration of renewable energy sources in smart grids by means of evolutionary optimization algorithms. Expert Systems with Applications, 39(5), 5513–5522.View ArticleGoogle Scholar
 Ameli, A., Bahrami, S., Khazaeli, F., & Haghifam, M. R. (2014). A multiobjective particle swarm optimization for sizing and placement of DGs from DG owner’s and distribution company’s viewpoints. IEEE Transactions on Power Delivery, 29(4), 1831–1840.View ArticleGoogle Scholar
 Baran, M. E., & Wu, F. F. (1989). Optimal sizing of capacitors placed on a radialdistribution system. IEEE Transactions on Power Delivery, 4(1), 735–743.View ArticleGoogle Scholar
 DoagouMojarrad, H., Gharehpetian, G. B., Rastegar, H., & Olamaei, J. (2013). Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm. Energy, 54, 129–138.View ArticleGoogle Scholar
 ElFergany, A. (2015). Study impact of various load models on dg placement and sizing using backtracking search algorithm. Applied Soft Computing, 30, 803–811.View ArticleGoogle Scholar
 Esmaeilian, H. R., & Fadaeinedjad, R. (2015). Energy loss minimization in distribution systems utilizing an enhanced reconfiguration method integrating distributed generation. IEEE Systems Journal, 9(4), 1430–1439.View ArticleGoogle Scholar
 Gampa, S. R., & Das, D. (2015). Optimum placement and sizing of DGs considering average hourly variations of load. International Journal of Electrical Power & Energy Systems, 66, 25–40.View ArticleGoogle Scholar
 Golshannavaz, S. (2014). Optimal simultaneous siting and sizing of DGs and capacitors considering reconfiguration in smart automated distribution systems. Journal of Intelligent & Fuzzy Systems, 27(4), 1719–1729.Google Scholar
 Hegazy, Y. G., Othman, M. M., ElKhattam, W., & Abdelaziz, A. Y. (2014). Optimal sizing and siting of distributed generators using big bang big crunch method. In 49th international universities power engineering conference (UPEC) (pp. 1–6).Google Scholar
 Hosseini, S. A., Madahi, S. S. K., Razavi, F., Karami, M., & Ghadimi, A. A. (2013). Optimal sizing and siting distributed generation resources using a multiobjective algorithm. Turkish Journal of Electrical Engineering and Computer Sciences, 21(3), 825–850.Google Scholar
 Hung, D. Q., Mithulananthan, N., & Bansal, R. C. (2010). Analytical expressions for DG allocation in primary distribution networks. IEEE Transactions on Energy Conversion, 25(3), 814–820.View ArticleGoogle Scholar
 Kolenc, M., Papic, I., & Blazic, B. (2015). Assessment of maximum distributed generation penetration levels in low voltage networks using a probabilistic approach. International Journal of Electrical Power & Energy Systems, 64, 505–515.View ArticleGoogle Scholar
 Mirjalili, S. (2016). Dragonfly algorithm: a new metaheuristic optimization technique for solving singleobjective, discrete, and multiobjective problems. Neural Computing and Applications, 27(4), 1053–1073.View ArticleGoogle Scholar
 Moradi, M. H., & Abedini, M. (2011). A combination of genetic algorithm and particle swarm optimization for optimal dg location and sizing in distribution systems. International Journal of Electrical Power & Energy Systems, 33, 66–74.Google Scholar
 Murthy, V. V. S. N., & Kumar, A. (2013). Comparison of optimal dg allocation methods in radial distribution systems based on sensitivity approaches. International Journal of Electrical Power & Energy Systems, 53, 450–467.View ArticleGoogle Scholar
 Murty, V. V. S. N., & Kumar, A. (2014). Mesh distribution system analysis in presence of distributed generation with time varying load model. International Journal of Electrical Power & Energy Systems, 62, 836–854.View ArticleGoogle Scholar
 Naik, S. G., Khatod, D. K., & Sharma, M. P. (2013). Optimal allocation of combined dg and capacitor for real power loss minimization in distribution networks. International Journal of Electrical Power & Energy Systems, 53, 967–973.View ArticleGoogle Scholar
 Nekooei, K., Farsangi, M. M., NezamabadiPour, H., & Lee, K. Y. (2013). An improved multiobjective harmony search for optimal placement of DGs in distribution systems. IEEE Transactions on Smart Grid, 4(1), 557–567.View ArticleGoogle Scholar
 Rahim, S. R. A., Musirin, I., Othman, M. M., Hussain, M. H., Sulaiman, M. H., & Azmi, A. (2013). Effect of population size for dg installation using EMEFA. In IEEE 7th international power engineering and optimization conference (PEOCO) (pp. 746–751).Google Scholar
 Rahim, S. R. A., Musirin, I., Sulaiman, M. H., Hussain, M. H., & Azmi, A. (2012). Assessing the performance of DG in distribution network. In 2012 IEEE international power engineering and optimization conference (PEDCO) Melaka, Malaysia (pp. 436–441).Google Scholar
 Reddy, P. D. P., Reddy, V. C. V., & Manohar, T. G. (2016). Application of flower pollination algorithm for optimal placement and sizing of distributed generation in distribution systems. Journal of Electrical Systems and Information Technology, 3(1), 14–22.View ArticleGoogle Scholar
 Dinakara Prasad Reddy, P., Veera Reddy, V. C., & Gowri Manohar, T. (2017a). Optimal renewable resources placement in distribution networks by combined power loss index and Whale optimization algorithms. Journal of Electrical Systems and Information Technology. https://doi.org/10.1016/j.jesit.2017.05.006.Google Scholar
 Dinakara Prasad Reddy, P., Veera Reddy, V. C., & Gowri Manohar, T. (2017b). Ant Lion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems. Journal of Electrical Systems and Information Technology. https://doi.org/10.1016/j.jesit.2017.06.001 Google Scholar
 Reddy, P. D. P., Reddy, V. C. V., & Manohar, T. G. (2017c). Whale optimization algorithm for optimal sizing of renewable resources for loss reduction in distribution systems. Renewables: Wind, Water, and Solar, 4(1), 3.View ArticleGoogle Scholar
 Su, C. L. (2010). Stochastic evaluation of voltages in distribution networks with distributed generation using detailed distribution operation models. IEEE Transactions on Power Systems, 25(2), 786–795.View ArticleGoogle Scholar
 Zangiabadi, M., Feuillet, R., Lesani, H., HadjSaid, N., & Kvaloy, J. T. (2011). Assessing the performance and benefits of customer distributed generation developers under uncertainties. Energy, 36(3), 1703–1712.View ArticleGoogle Scholar