**Open Access**-
**Authors :**Karimulla. Pollisetti, Ch Hariprasad -
**Paper ID :**IJERTCONV8IS16043 -
**Volume & Issue :**NCSMSD – 2020 (Volume 8 – Issue 16) -
**Published (First Online):**18-10-2020 -
**ISSN (Online) :**2278-0181 -
**Publisher Name :**IJERT -
**License:**This work is licensed under a Creative Commons Attribution 4.0 International License

#### Impact on Radial Distribution System by Integrating Wind Power with ZIP Load Considering Load Growth

Karimulla. Pollisetti

Department of EEE Bapatla Engineering College,

Bapatla, India

Ch Hariprasad

Department of EEE Bapatla Engineering College

Bapatla, India

AbstractIntegration of wind based generation into the distribution system has significantly grown over few years. In this paper voltage sensitivity index (VSI) method has been taken for optimal placement of wind based distribution generation (DG) in radial distribution network. The main contribution of the paper is: (a) modeling of wind based generator (b) optimal placement of wind based DG using VSI method with constant load (c) optimal placement of wind based DG using VSI method with ZIP load, (d) optimal placement of wind based DG using VSI method in presence of Load Growth.Voltage profile, the real and reactive powers intake by the grid, real and reactive power losses are determined. The result show the importance of installation of wind based DG at the suitable location. The entire results are obtained on the IEEE 33-bus test system.

Keywords Distributed generation; Voltage sensitivity index; ZIP load; Radial distribution system.

INTRODUCTION

In recent years due to rapid depletion of fossil fuel resources, renewable energy (RE) based DG were developing fast as they diminish power losses, power quality issues, carbon emission and also provide economical benefits. Many power companies are investing in renewable energy resources such as wind, photovoltaic cells, hydro-turbines with the distribution generation (DG), the main objective is to minimize total system power losses. Generating plant that serve a customer on-site or provide support to a distribution network is defined as Distributed generation (DG). Distributed generation (DG) can be used in isolated way or in integrated way. Wind turbine is the most promising DG technology among renewable energy sources. The integration of wind based DG offers the environmental and economical benefits and affects the system operating characteristics such as electric losses, voltage profile, stability and power system operation. Reduction in overall system losses and improve

dispatchable renewable DGs are considered for minimization of annual energy losses. For this aim, optimal size and power factor of DG is obtained by analytical approach then renewable DG are placed at optimal location for minimizing energy losses. In Reference [4] minimum power losses, reliability and voltage profile improvement are obtained by proper allocation of DGs in distribution system based on dynamic programming. Cost of DGs in radial distribution system is determined in [7] based on conventional, complex and triangular power limits. Optimal placement of DG in radial distribution system by analytical method to improve power losses are presented in [15]. In Reference [17] analytical method is proposed to determine the location, size and power factor of renewable DG to minimize energy losses.

In this paper, voltage sensitivity index method is proposed to find the optimal location of wind-based DG in Radial distribution (RD) system. Wind turbine is used as a DG and optimal size and location of DG source is calculated to reduce the system losses and to improve voltage profile. Load growth factor is also considered and the results are obtained on IEEE 33-bus. This paper sectioned into: 1.Modeling of wind based generator; 2. optimal placement of wind based DG using VSI method with constant load; 3.optimal placement of wind based DG using VSI method with ZIP load; 4.optimal placement of wind based DG using VSI method in presence of load growth.

PROPOSED METHODOLOGY AND MODELING OF SYSTEM

Modeling of Wind- based Generator

The wind power can be converted into electrical energy by wind turbine and the output power of wind turbine is given as

voltage profile of system is obtained by optimal placement of 1 3

DGs in distributed system.

In literature, various methodologies have been developed for optimum location and size of DG. In Reference [1] optimal placement and size of DGs is determined by the PSO technique. Different types of DGs at different buses are considered for optimal power factor, real and reactive power by minimizing power distribution loss. PSO and analytical approach for optimal placement and size of DGs are proposed by author to minimize the power losses of system. For this, loads are sampled in small steps for each step optimal size and location of different DGs is obtained on IEEE-33 bus system in [2]. In Reference [3] dispatchable and non-

Pout 2 Cp A Where CP is the turbine power coefficient; A is area swept by rotor blades in m2; is air density and its value is

1.266 kg/m2.

The output power of wind turbine depends on wind speed at the site as well as the parameters of power performance curve. The output power during different states is calculated as in (2)

0, i

pattern of wind velocity for six levels of wind speed and

p

n , i r

Table II shows the wind turbine output.

out r

1, r

0,

n

n

i

r i

Where pout is power output of wind turbine (KW or MW); i is cut in speed; r is the rated speed (m/s); is cut

out speed of wind turbine; r is rated power in MW and

n is the normalized power output in non-rated region. The values for wind parameters are given in Table I.

TABLE I. WIND TURBINE PARAMETERS

Wind Turbine Parameters

Rated power (MW)

Cut-in speed (m/s)

Rated speed (m/s)

Cut-out speed (m/s)

2

3

11.5

20

(a ) Weibull Modelling

Here, wind speed modeling is done by Weibull pdf [4]. The probability distribution function of Weibull distribution is given by (3)

Fig. 1. Wind velocity pattern.

TABLE II. WIND TURBINE OUTPUT

Wind levels

Speed range (m/s)

%age of turbine o/p power (%)

Probability of occurrence

Mean o/p power (MW)

Power o/p (MW)

0

0-5

0

0.1416

0

0

1

5-8

12.0399

0.1683

0.2408

0.0405

2

8-10

40.5411

0.2636

0.8108

0.2137

3

10-12.5

77.8208

0.2250

1.5564

0.3502

4

12.5-20

100.000

0.1219

2.0000

0.2437

5

20-25

100.000

0.0654

2.0000

0.1307

Voltage Sensitivity Index [VSI] Method

fv

k v

c c

c c

k 1

v k

e c 0 v

This method is used to find optimal placement of wind- based DGs. Voltage sensitivity index is a numerical solution in which the operator knows how close the system is to collapse. This idex is evaluated at all branches in radial

Where, v is the wind speed (m/s); k is the shape index and

c is scale index. In paper the value of k is 1.75 and c is 8.78.

1.086

distribution (RD) system by using (8)

n 1V 2

k

VSI

k 1 k

m

Where vm is mean wind speed in m/s and is standard deviation of wind speed.

n

Where n is number of buses or nodes and k is set of nodes.

For stable operation of system the VSI should be less than unity. The bus with high sensitivity index is the most stable

c m

1 1

bus and selected for the placement of wind-based DG.

ZIP Load Model

k

Also weibull probability distribution function is applied on the wind system to find the probability of wind speed occurrence for these 20000 samples are taken at random and

The load at each node is modeled as a combination of constant impedance (Z), current (I) and power (P). ZIP load is calculated by (9) and (10)

are divided into six levels. The probability of occurrence of

t npr t npc

t npi

wind speed levels is given by

P t t a t i b t i

c t i

di di

prob NW

TW t nqr

t nqc t nqi

Q t Q t a t i

b t i c t i

Where, NW is number of wind samples and TW is total

di di

number of wind samples at a given level. Fig. 1 shows the

Where, a(t), b(t) and c(t) are fraction of constant impedance, current and power at time t are 0.2,0.2,0.6

respectively. The parameters npr, npc and npi are active power exponents 2, 1, 0 respectively to get ZIP load and reactive power exponents are same as active power exponents.

Load Growth factor

Increase in load demand increases the system power losses and voltage drop, so for efficient operation of distribution system the future expansion and planning of distribution system is desired to be estimated. Load Growth is given by

m

m

Loadi Load 1 r

Where, r is annual load growth rate and m is period of load plan which is specified by the feeder. In this paper, 7.5

% load growth rate and 5 years for planning are taken for the study.

Fig. 3. Voltage profile for constant load

RESULTS AND DISCUSSION

In this paper, results are obtained for IEEE-33 bus Radial Distribution (RD) system with forward backward sweep load flow method. Voltage Sensitivity Index (VSI) method is used to find the optimal placement of wind-based DGs for voltage profile improvement and to reduce system losses. The base MVA and base KVAR for the test system are 100 MVA and

12.66 KVAR respectively.

The results are obtained by placing wind-based DG to obtain voltage profile, total power losses, each branch real and reactive power losses for constant load, ZIP load and Load Growth on IEEE-33 bus system.

Voltage Sensitivity index [VSI]

Voltage sensitivity index is a numerical solution in which the operator knows how close the system is to collapse. The bus having high sensitivity index is selected for the placement of DG. Fig. 2 shows the VSI plot for IEEE-33 bus system.

Fig. 2. VSI plot for IEEE-33 bus system.

Here VSI values are obtained for IEEE-33 bus and 18th bus is selected for the optimum placement of wind-based DG because it has high VSI as shown in Fig. 2.

Effect of Wind-based DG on Voltage Profile

The integration of wind-based DG increases the voltage profile of system. Voltage profile with wind-based DG and without DG for constant load, ZIP load and Load Growth are shown respectively in Figs. 3, 4, 5.

Fig. 4. Voltage profile for ZIP load

Fig. 5. Voltage profile for Load Growth

The Voltage profile obtained for constant load, ZIP load and Load Growth for Wind-based DG2 is shown in Fig. 6.

Fig. 6. Voltage profile for constant load, ZIP load and Load Growth for wind-based DG2

From the figures 3,4,5and 6, it is observed that voltage profile is improved with placement of wind-based DG and voltage values are reduced considering Load Growth due to its increased load demand.

Effect of Wind-based DG on Total Power Losses

The optimum allocation of wind-based DG by VSI is at 18th bus by placing DG at 18th bus and varying the levels of wind-based DG power stepwise, the variation of total power losses is obtained. Table III, IV shows real and reactive power losses for different loads without wind or with wind speed levels respectively.

TABLE III. TOTAL REAL POWER LOSSES

Wind DG

Total Real Power Losses (KW)

Constant load

ZIP load

Load Growth

w/o wind

210.9824

192.7107

319.1046

Level1

204.4866

186.6089

310.6467

Level2

180.9839

164.6837

279.2783

Level3

167.0252

151.8585

259.7145

Level4

177.5843

161.5409

274.6075

Level5

191.4042

174.3687

293.3395

TABLE IV. TOTAL REACTIVE POWER LOSSES

Wind DG

Total Reactive Power Losses (KVAR)

Constant load

ZIP load

Load Growth

w/o wind

143.0219

130.411

216.4626

Level1

138.2395

125.9236

210.2367

Level2

121.2822

110.1444

187.5125

Level3

111.6499

101.3537

173.7864

Level4

718.8936

107.9472

184.1944

Level5

128.7198

117.0336

197.6167

The real and reactive power losses for the IEEE-33 bus are 210.9824 KW and 143.0219 KVAR respectively without installation of DG and 204.4866 KW and 138.2395 KVAR with wind-based DG1 respectively. From this it is observed that losses are reduced with DG and for each level it varies according to the wind power output.

Effect of Wind-based DG on Real and Reactive Power loss in each branch

The integration of DG sources at the various location of distribution system lead changes in the characteristics of the system network with more variable real and reactive power flow. The results are obtained for constant load, ZIP load and Load growth for IEEE-33 bus system.

Real and Reactive power loss with constant load

Fig. 7 and Fig. 8 shows Real and Reactive power loss with constant load, which show maximum power loss without installation of wind-based DG and minimum loss with wind- based DG. Wind-based DG which has the maximum power output i.e. level3 gives less real and reactive power loss.

Fig. 7. Real power loss for IEEE-33 bus with constant load

Fig. 8. Reactive power loss for IEEE-33 bus with constant load

Real and Reactive power loss with ZIP load

Fig. 9, 10 shows Real and Reactive power loss for DG with wind power and without wind. Here without wind DG, system gives maximum losses and minimum losses are obtained for level3 which is having maximm power considering ZIP load.

Fig. 9. Real power loss for IEEE-33 bus with ZIP load

Fig. 10. Reactive power loss for IEEE-33 bus with ZIP load

Real and Reactive power loss with Load growth

Radial distribution system when integrated with wind- based DG considering load growth give maximum real and

reactive losses. Fig. 11, 12 shows Real and Reactive power loss for Load Growth considering constant load.

Fig. 11. Real power loss for IEEE-33 bus with Load Growth

Fig. 12. Reactive power loss for IEEE-33 bus with Load Growth

The real and reactive power losses for constant load, ZIP load and Load Growth for wind-based DG1 are shown in Fig. 13 and 14.

Fig. 13. Real power loss with constant load, ZIP load and Load Growth for wind DG1

Fig. 14. Reactive power flow with constant load, ZIP load and Load Growth for wind DG1

From above Fig. 13 and 14 it is observed that level1 wind-based DG with ZIP load gives minimum real and reactive power losses and maximum losses are obtained for considering Load Growth. Here ZIP load is voltage dependent load, based on the bus voltages load at each bus is varied according to that losses in each branch varies.

Effect of Wind-based DG on Cost of Energy Losses

Annual cost of energy loss is calculated by total real power losses and energy rate. Cost of energy loss is calculated by (12)

CostEL= (Total Active Power Loss) Ã— (EcÃ—t) (12) Where Ec is energy rate ($/KWh) and t is time duration (h), the value of Ec and t taken for study are 0.06 $/KWh and

8760 h respectively.

Total real power losses are reduced with installation of wind-based DG due to this there is a significant decrease in cost of energy losses. Here, the cost of energy losses without wind-based DG is 110.8923 $ and 107.4782 $ with installation of wind-based DG1 respectively. Table V shows the cost of energy losses without DG and with DG considering constant load, ZIP load and Load Growth.

TABLE V. COST OF ENERGY LOSSES

Wind DG

Cost of Energy Losses ($)

Constant load

ZIP load

Load Growth

w/o wind

110892.3

101288.7

167721.4

Level1

107478.2

98081.6

163275.9

Level2

95125.1

86557.8

146788.7

Level3

87788.4

79816.8

136505.9

Level4

93338.3

84905.9

144337.0

Level5

100602.0

91648.2

154179.2

CONCLUSION

Here in this paper results are obtained on IEEE-33 bus Radial Distribution (RD) system. Optimal placement of wind- based DG is obtained by Voltage Sensitivity Index (VSI) method and wind generator is modeled by using weibull probability distribution function. Load Growth is considered for IEEE-33 bus standard system for future planning. This integration of wind-based DG in the system is done to improve the voltage profile and to reduce power losses with constant load and ZIP load and also considering load Growth. The real and reactive power losses for IEEE-33 bus are 210.9824 KW and 143.0219 KVAR respectively, without installation of DG and 204.4866 KW and 138.2395 KVAR with wind-based DG1 respectively. From this it is concluded that losses are reduced and voltage profile is improved with wind-based DG comparing to without wind DG. With ZIP load minimum power losses are obtained and maximum losses are obtained for considering Load Growth for both the cases with and without wind-based DG. In the Future the same analysis can be done on mesh distribution system by integrating both wind and solar-based DGs.

REFERENCES

S. Kansal, V. Kumar and B. Tyagi, Optimal placement of different type of DG sources in distribution networks, Electric Power and Energy System, vol. 53, pp. 752-760, December 2013.

P. Karimyan, G.B Gharehpetian, M. Abedi and A. Gavili, Long term scheduling for optimal allocation and sizing of DG unit considering load variations and DG type, Electric Power and Energy System, vol. 54, pp. 277-287, January 2014.

D.Q Hung, N. Mithulananthan, K.Y Lee, Optimal placement of dispatchable and nondispatchable renewable DG units in distribution networks for minimizing energy loss, Electric Power and Energy System, vol. 55, pp. 179-186, February 2014.

N. Khalesi, N. Rezaei and M.R. Haghifam, DG allocation with application of dynamic programming for loss reduction and reliability improvement, Electric Power and Energy System, vol. 33, pp. 288- 295, February 2011.

V.V.S.N. Murthy, Ashwani Kumar, Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth, Electric Power and Energy system, vol. 69, pp. 246-256, 2015.

J.A. Carta, P. Ramirez and S. Velazquez, A review of wind speed probability distribution used in wind energy analysis: Case studies in the Canary Islands, Renewable Energy and Sustainable Energy Reviews, vol. 13, pp. 933-955, June 2009.

S. Hasanpour, R. Ghazi and M.H. Javidi, A new approach for cost allocation and reactive power pricing in a deregulated environment, Electrical Eng Springer-verlag, vol. 19, pp. 27-34, June 2009.

S.H. Pishgar-komleh, A. keyhari and P. Sefeedpari, Wind speed and power density analysis based on Weibull and Rayleigh distribution (a case study: Firouzkooh country of Iran), Renewable and Sustainable Energy Reviews, vol. 42, pp. 313-322, 2015.

V.V.S.N. Murthy, Ashwani Kumar, Mesh distribution system analysis in presence of distributed generation with time varying load model, Electric Power and Energy system, vol. 62, pp. 836-854, 2014.

M.P. Lalitha, V.C.V. Reddy and V. Usha, Optimal DG placement for minimium real power loss in radial distribution systems using PSO, J. Theor Appl Inform Technol, vol. 13, pp. 107-16, 2010.

M.M. Aman, G.B. Jasmon, H. Mokhills and A.H.A. Bakar, Optimal placement and sizing of a DG based on a new power stability index and line losses, Electric Power and Energy System, vol. 43, pp. 1296- 1304, 2012.

M.H. Moradi, M. Abedini, A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution system, Electric Power and Energy System, vol. 34, pp. 66-74, 2012

C.L.T. Borges, D.M. Falcao, Optimal distributed generation allocation for reliabilty, losses and voltage improvement, Electric Power and Energy System, vol. 28, pp. 413-420, 2006.

F. Ugramli, E. Karatepe, Optrimal wind turbine sizing to minimize energy loss, Electric Power and Energy System, vol. 53, pp. 656-663, 2013.

C. Wang, M.H. Nehrir, Analytical approaches foe optimal placement of distributed generation sources in Power systems, IEEE Transaction on Power System, vol. 19, November 2004.

F. Rotaru, G. Chicco, G. Grigoras and G. Cartina, Two-stage distributed generation optimal sizing with clustering-based node selection, Electric Power and Energy System, vol. 40, pp. 120-129, september, 2012.

L. Dulan, M. Abrudean and D. Bica, Distributed generation technologies and optimiization, Procedia Technology, vol. 12, pp. 687-692, 2014.