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ScienceDirect Procedia Technology 25 (2016) 692 – 701

Global Colloquium in Recent Advancement and Effectual Researches in Engineering, Science and Technology (RAEREST 2016)

PV-WT based distribution generator location minimizing transmission loss in Pool/Bilateral electricity market model Manish Kumara , Ashwani Kumarb , K. S Sandhuc* a

National Institute of Technology, Kurukshetra , 136119, India National Institute of Technology, kurukshetra, 136119, India

bc

Abstract In this paper, analysis has been carried out for transmission loss minimization with the integrated hybrid PV and Wind turbine in the power system network. For obtaining the power output from the PV and Wind turbine, a probabilistic model for the solar irradiation and wind has been considering using Beta and Weibull distribution function. A Mixed Integer Nonlinear Programming (MINLP) approach has been utilized for determining optimal location and number of distributed generators considering minimization of transmission loss. The analysis has been carried out with constant PQ load and Zip load model. The impact of different load models has been studied. The analysis has been carried out for IEEE24 bus test system in pool/bilateral electricity market model. ©2016 2015The TheAuthors. Authors. Published Elsevier © Published by by Elsevier Ltd.Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of RAEREST 2016. Peer-review under responsibility of the organizing committee of RAEREST 2016

Keywords: Transmission loss minimization, mixed integer non-linear programing(MINLP) approch, Distbution generator, electricity market;

1. Introduction Day by Day, the world energy demand is increasing at a larger rate because the growth of human population increases. The 1, 4 billion people still do not have access to energy services [1]. The problem of lack of interconnected electrical grid and synchronous power system in remote areas can be overcome by the connection of renewable renewable energy resource based distributed generation into the grid. In present day, there are a number of DG technologies available in the market and a few are still at the research and development stage. Among

* Corresponding author. Tel.: +911744233389; fax: +911744238050. E-mail address: [email protected]

2212-0173 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of RAEREST 2016 doi:10.1016/j.protcy.2016.08.162

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available technologies, wind and solar based DG technologies are going to lead the electricity market because of their environmental friendly characteristic and no end of resource availability. According to the advanced scenario wind and solar power could reach nearly 2000GW and 1600-2000 GW by 2030, it supplies approximately 18.8% of global electricity and helps to save 3 billion tons of CO2 emissions annually [2]. The integrated hybrid Wind and PV based distributed generation (DG) are best alternative solution to fulfill the energy demand. When a DG is connected to the transmission network, it overcomes transmission losses, improve voltage profile and the system reliability, distribution and transmission system congestion management, operating cost and gives better quality of power. But the main challenge for researchers is to find optimal locations, size and management strategic of DGs. Many researchers implement many optimization algorithm for optimal location and size of DGs. DG optimization is based on intelligent control methods such as simulated annealing, particle swarm optimization(PSO), genetic algorithm and Tabu search, Noval approach, Ant Colony Optimization, Evolutionary Programming , Mixed integer programming , and some other heuristic approaches , of course other studies have also been presented based on analytical method in optimization of DG for power loss reduction [3-10]. The evaluation of integrated hybrid system has been reported using different performance models, optimization software tools and techniques [11, 12]. In [13-15], the optimal scheduling, energy management of hybrid system is discussed and also focused on mixed-integer linear programming (MILP) formulations to optimize micro-grid. In [16], was proposed optimal of wind and solar based DGs in distribution system for power loss minimization and voltage stability improvement by using PSO. The main contribution of this work is to provide an integrated hybrid wind and PV based distributed generation to connect the transmission network and for reducing the transmission loss of the networks. A mixed integer nonlinear programming (MINLP) approach for determining optimal location and number of distributed generator considering minimization of transmission loss with the presence of integrated hybrid Wind and PV based DG. The total real and reactive power loss, percentage reduction in active power loss, optimal DG location has been obtained. The results have also been obtained for minimization of transmission loss with zip load. The results are obtained for bilateral/ pool electrical market model. The proposed MINLP based optimization approach has been applied for IEEE 24 bus reliability test system. The optimization problem has been solved using MTLAB and GAMS interfacing with DICOPT solver in GAMS. 2. Modeling of Renewable resource and load data 2.1. Historical data processing The Weibull and Beta probability distribution function those are required for estimating the hourly wind speed and solar irradiance respectively. Beta probability density function (PDF) based on historical data which have been collected for three year and Weibull probability distribution based on data for the hourly mean with speed during the month of May over the first twelve year (1994-2005) for model synthesis. The solar irradiance sample and wind speed samples produced by MCS. In Monte Carlo method a simulation can typically involves over 20000 evaluations of the model, a task which is computationally expensive. 2.2. Solar irradiation modeling. The Beta probability density functions PDF are used for probabilistic nature of solar irradiances [17]. Many studies have been done for modelling of PV model such as [18, 19]. The PDF for solar irradiance s can be expressed as follows: ௰ሺఈାఉሻ ሺఈିଵሻ ሺͳ െ ‫ݏ‬ሻሺఉିଵሻ ǡͲ ൑ ‫ ݏ‬൑ ͳǡ ߙǡ ߚ ൒ Ͳ ‫ݏ‬ ݂௕ ሺ‫ݏ‬ሻ ൌ ቊ ௰ሺఈሻ௰ሺఉሻ (1)  Ͳ‫݁ݏ݅ݓݎ݄݁ݐ݋‬ 2 Where ݂௕ ሺ‫ݏ‬ሻ : Beta distribution function of s, s: random variable of solar irradiance (kW/m ), ߙ and ߚ : parameters of݂௕ ሺ‫ݏ‬ሻ, which are calculated using the meanሺߤሻ and standard deviation ሺߪሻ of solar irradiance s as follows: ߚ ൌ ሺͳ െ ߤሻ ቀ

ఓሺଵିఓሻ ఙమ

െ ͳቁ

(2)

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ߙൌ

ఓൈఉ

(3)

ଵିఓ

2.3. Wind speed modeling The power of Wind turbine depends on the wind speed in the site. Wind speed varies every minute, hour, day and season of the year. There are various methods to model wind behaviour studies such as [20, 21]. Where k and c are the shape and scale factor of the Weibull PDF of wind speed [22], ߪ and ߤ are mean m/s and standard devation m/s. In this work, we used Webull PDF modeled for variation of wind speed v and the data for the hourly mean with speed during the month of May over the first twelve year (1994-2005) in [23]. The output of a WT as follows: ௩ ሺ௞ିଵሻ

௞

ܲ‫ܨܦ‬ሺ‫ݒ‬ሻ ൌ ቀ ቁ ቀ ቁ ௖ ఙ ିଵǤ଴଼଺

௖

௩ ௞

݁‫ ݌ݔ‬൤െ ቀ ቁ ൨

(4)

௖

݇ൌቀ ቁ

(5)

ఓ

The proposed MINLP approach for an optimal distribution generation location has been applied to IEEE 24-bus reliability test system [24]. The two type of load with constant P, Q load model and with variable zip load at each bus. 3. Calculation of output power of PV module and Wind turbine power We have use four type PV module and four type Wind turbine; it is refer to [21]. In PV-based DG system, the rating of the each PV modules is very small as compared to the required amount of PV-based DG. So we are using a PV panel consisting of 20600 modules according [21]. The output power of each module (module A, module B , module C, module D) are obtained 24 hours based on three years of the collected data and the 24 hours output power of the each PV panel. But we have use the average power output of day in the every PV panel. The average value of active and reactive power generation of each PV plant, PV plant A active power is 0.1814 MW and reactive power is 0.0258 MW, PV plant B active power is 0.1863 MW and 0.0265 MW are reactive power, PV plant C active power is 0.2162 MW and reactive power is 0.0308 MW, and PV plant D active power is 0.3348 MW and 0.047 MW are the reactive power of the PV plant. The average value of active and reactive power generation of each turbine, turbine A active power is 0.0848 MW and reactive power is 0.0121 MW, turbine B active power is 0.2171 MW and 0.0309 MW are reactive power, turbine C active power is 0.1308 MW and reactive power is 0.0186, and turbine D active power is 0.3254 MW and 0.0464 are the reactive power of the turbine. 4. General OPF formulation General objective of MINLP approach





Min F y, h, [ Subject to equality and inequality constraints defined as

 

int

(6)



u y, h, [ int 0 g y, h, [ int d 0

(7) (8)

where, y is state vector of variables V, δ; , h are the control parameters, Pgk,Qgk, P(PV-WT)k, Q(PV-WT)k; , [ is an integer variable with values (0,1). The zero means without and one means with distributed generator in the network. Objective function F is int





Min F y, h, [ ܲ௞௝௟ ൅ ܲ௝௞௟ The line flows from bus-k to bus-j and bus-j to bus-k are given as: int

Pkjl Vk2 Gkj  Vk V j Gkj cosG k  G j  Bkj sinG k  G j

Pjkl V j2Gkj  VkV j Gkj cosG k  G j  Bkj sinG k  G j

(9) (10) (11)

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4.1. Equality constraints Distributed generation presence the modified power flow balance equations of equality constraints both real and reactive power equations for all the buses as

Pk

Pgk  [ kint P( PV WT ) k  Pdk  k 1,2,  Nb

(12)

Qk Qgk  [ kint Q( PV WT ) k  Qdk k 1,2, Nb

¦V V >G Nb

Pk

k

j

kj

(13)

@

cosG k  G j  Bkj sin G k  G j  k 1,2, N b

(14)

j 1

¦V V >G Nb

Qk

k

j

kj

@

sin G k  G j  Bkj cosG k  G j  k 1,2, N b

(15)

j 1

Bilateral constraints are:

Pdk Pgk

Pdbk  Pdpk  k 1,2, N b Pgbk  Pgpk  k 1,2, N b

(16) (17)

Where Pdbk and Pdpk are the bilateral and pool demand Pgbk and Pgpk are the bilateral and pool conventional generation

Pdbk Pgbk

¦ GD(k, j)  k ¦ GD(k, j)  k k

1,2, N b , j 1,2, N b

(18)

j

1,2, N b , j 1,2, N b

(19)

Where GD is the bilateral transaction matrix. 4.2. Inequality constraints (a) Real power generation limit of generators at bus-k

Pgkmin d Pgk d Pgkmax , k 1,2, N g

(20)

(b) Reactive power generation limit at bus-k min max Qgk d Qgk d Qgk , k 1,2, Nq

(21)

(c) Voltage limit at bus-k

Vkmin d Vk d Vkmax , i 1,2, Nb

(22)

(d) Phase angle limit at bus-k

G kmin d G k d G kmax , k 1,2, Nb

(23)

(e) Bilateral transaction GD matrix at bus-k

GD(k , j ) d GD max(k , j )k 1,2, Nb, (24) (f) Line flow limits: These constraints represent maximum power flow in a transmission line and are based on thermal and stability considerations. The line flow limit can be written as: Skj d Skjmax 4.3. Power generation limit a) Real power generation limit max P(min PV WT ) k d P( PV WT ) k d P( PV WT ) k , k 1,2,  N ( PV WT )

(25)

696

where,

Manish Kumar et al. / Procedia Technology 25 (2016) 692 – 701 max P(min PV WT ) k , P( PV WT ) k are the minimum and maximum generation limits.

b) Reactive power generation limit of distributed generators at bus-k max Q(min PV WT ) k d Q( PV WT ) k d Q( PV WT ) k , i 1,2,  N ( PV WT )

where,

(26)

max Q(min PV WT ) k , Q( PV WT ) k are the minimum and maximum generation limit.

c) Optimal number of distributed generators in the network.

N ( PV WT )

N ( pv wt ) int

¦[

k

d N (max PV WT )

(27)

k 1

4.4. Without PV-WT based DG The real and reactive power injection equations can be modified in the presence of zip load as:

Pk

Pgk  Pdzk k 1,2, Nb

(28)

Qk

Qgk  Qdzk k 1,2, Nb

(29)

4.5. With PV-WT based DG With distributed generation from equation (21) and (22) the real and reactive power constraints are modified in the presence of zip load as:

Pk

Pgk  [ kint P( PV WT ) k  Pdzk  k 1,2, N b

Qk Qgk  [ Q( PV WT ) k  Qdzk k 1,2, N b int k

(30) (31)

5. Results and discussion The results have been obtained for voltage profile, total real and reactive power loss, PV-Wind based DG output and %age reduction in the transmission loss with PV-Wind based DGs. The results are also obtained with zip load variation at each bus for comparison with constant P, Q load model. The results are given in tabular form in tables. Results of different cases without and with optimally located PV-Wind based DGs at the selected buses have been obtained and it categorized as: Case 1: Without PV-Wind based distributed generator Case 2: With one PV-Wind based distributed generator Case 3: With two PV-Wind based distributed generators Case 4: With three PV-Wind based distributed generators 5.1. Results for minimization of transmission loss with constant load

The simulation of transmission loss minimization has been determined by solving nonlinear optimization problem as explained in section 4. The results for minimization of constant load are given in Table1 which contains the total active and reactive loss named as PLT and QLT respectively. It also represents the percentage reduction in total active power loss which is calculated by the following formula ௉௅்ೢ೔೟೓೚ೠ೟೏೒ ି௉௅்ೢ೔೟೓೏೒ %reduction in loss = ൈ ͳͲͲ% (32) ௉௅்ೢ೔೟೓೚ೠ೟೏೒

In case 1, without Wind-PV based DG the total active and reactive power loss in the system are 0.2877p.u.MW and -2.9975p.u.MVar respectively. The total active and reactive load is 28.5(p.u.MW) and 5.8p.u.MVar respectively which remain constant in each case. In case 2 (with one Wind-PV based DG), the total active and reactive power losses are 0.2608 p.u.MW and -3.3051p.u.MVar with the optimal bus location of Wind-PV based DG at bus 3. The

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percentage reduction in the loss is 9.35%. Table 1: Result for minimization of total transmission loss with constant load. Case 1:Without Case2:With Case3:With DG( PV+WT) 1DG( PV+WT) 2DG(PV+WT) PLT(p.u.MW) QLT(p.u.MVar) Minimum voltage Total active load(p.u.MW) Total reactive Load(p.u.MVar) %age reduction in loss Optimal Bus location of DG(PV+WT) Total DG(PV+WT) size(p.u.MW) Total DG (PV+WT) size(p.u.MVar)

Case4:With 3DG( PV+WT)

Case5:With 4DG(PV+WT)

0.2877 -2.9975 0.9661 (At bus 3) 28.5

0.2608 -3.3051 0.9673p.u. (At bus 4) 28.5

0.2370 -3.5335 0.9648 p.u (At bus 4) 28.5

0.2246 -3.6696 0.9640p.u (At bus 4) 28.5

0.2139 --3.7099 0.9600 p.u. (At bus 4) 28.5

5.8

5.8

5.8

5.8

5.8

9.35%

17.62%

21.93%

25.65%

3

3,10

3,5,10

3,5,6,10

0.6602

1.3204

1.7238

1.9900

0.0941

0.1041

0.1141

0.1141

In case 3 (with two PV-based DG), the total active and reactive power loss are 0.2370 p.u.MW and 3.5335p.u.MVar with the optimal bus location of DG at 3 and 10. The percentage reduction calculated is 17.62%. In case 4 (with three Wind-PV based DG), the total active and reactive power loss are 0.2246p.u.MW and 3.6696p.u.MVar with the optimal bus location of Wind-PV based DG at 3,5,10. The percentage reduction is 21.93%. In case 5 (with four Wind-PV-based DG), the total active and reactive power loss are 0.2139p.u.MW and 3.7099p.u.MVar with the optimal bus location of Wind-PV based DG at 3, 5, 6, and10. The percentage reduction obtained is 25.65%. In each case when number of Wind-PV based DG increases, it is observed that the percentage reduction in loss increases. This is attributed to the fact that generation is available locally and the power flow patterns changes in such a way that there is loss reduction in each line contributing to overall loss reduction in the system. The best case is found to be case-5 (with four Wind-PV based DGs). The voltage, PLT and Wind-PV based DG size and location are shown in Fig. 1, 2 and 3 respectively. 5.2. Results with ZIP load The effect of zip load has been determined modifying the power flow equation with the ZIP load as given in section 4. The results for the optimization problem with Zip load are given in Table 2. It have been obtained the total real and reactive power loss, size of DGs, total real and reactive power loads and %age reduction in the losses. In case 1 (without PV-Wind based DG) the total active and reactive power loss in the system are 0.2897 p.u.MW and 2.9647p.u.MVar respectively. The total active and reactive load are 28.6555 p.u.MW and 5.8317 p.u.MVar respectively and in each case the there is slight load change due to the change in the voltage profile due to the dependency of ZIP load model on voltage. In case 2 (with one PV-Wind based DG), the total active and reactive power loss are 0.2625 p.u.MW and -3.2599p.u.MVar with the optimal bus location of DG at bus 3. The percentage reduction obtained is 9.38%. In case 3 (with two PV-Wind based DGs), the total active and reactive power loss are 0.2369p.u.MW and -3.5133p.u.MVar with the optimal bus location of PV-Wind based DG at buses 3, 10. The percentage reduction obtained is 18.22%. In case 4 (with three PV-Wind based DG), the total active and reactive power loss are 0.2241 p.u.MW and -3.6345 p.u.MVar with the optimal bus location of PV-Wind based DG at bus 3, 5, 10. The percentage reduction obtained is 22.64%. In case 5 (with four PV-Wind based DGs), the total active and reactive power loss are 0.2113p.u.MW and -3.7089p.u.MVar with the optimal bus location of PV-Wind based DG is 3, 5, 6 and 10. The percentage reduction obtained is 27.06%. It is observed that in each case, with increase in

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Case1

1.06 1.04 1.02 1 0.98 0.96 0.94 0.92 0.9

Case2 Case5

Voltage p.u

Case4

Case3

1

3

5

7

9

Total active power loss(p.u MW)

0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 Case2

Case3

Case4

Case5

Fig.2. Total active power loss with and without PV-Wind based DG with constant load

Case1

Case2

Case4

Case5

3

5

7

9

Case3

11 13 15 17 19 21 23

Bus no Fig.4. Voltage profile with and without PV-Wind based DG with Zip load 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 Case1

Case2

Case3

Case4

Case5

Fig.5. Total active power loss with and without PV-Wind based DG with Zip load 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

PV-WT based DG Size p.u

PV-Wind based DG Size p.u

1

11 13 15 17 19 21 23

Bus no Fig.1. Voltage profile with and without PV-Wind based DG with constant load

Case1

1.06 1.04 1.02 1 0.98 0.96 0.94 0.92 0.9

Total active power loss(p.u MW)

Voltage p.u

number of PV-Wind based DGs, the percentage reduction in loss increases. The best case is found with four PVWind based DGs where the maximum reduction takes place in the total transmission loss. It is due to the fact that the power flow patterns with PV-Wind based DGs changes and the losses reduction takes place in each line thereby there is overall reduction in the losses of the system. The voltage, PLT and PV-Wind based DG size and location are shown in Figs. 4, 5 and 6 respectively.

Fig.3. PV-Wind based DG size in p.u with constant load

Fig.6. PV-Wind based DG size in p.u with Zip load

Table 2: Result for minimization of total transmission loss with Zip load. Case 1:Without Case2:With Case3:With DG( PV+WT) 1DG( PV+WT) 2 DG(PV+WT) PLT(p.u.MW) QLT(p.u.MVar) Minimum voltage Total active

0.2897 -2.9647 0.9648 (At bus 3) 28.6555

0.2625 -3.2599 0.9684 p.u. (At bus 4) 28.5918

0.2369 -3.5133 0.9665 p.u. (At bus 4) 28.5097

Case4:With 3DG( PV+WT)

Case5:With 4DG(PV+WT)

0.2241 -3.6345 0.9649p.u. (At bus 4) 28.4552

0.2113 --3.7089 0.9617 p.u . (At bus 4) 28.3633

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load(p.u.MW) Total reactive Load(p.u.MVar) %age reduction in loss Optimal Bus location of DG(PV+WT) Total DG(PV+WT) size(p.u.MW) Total DG (PV+WT) size(p.u.MVar)

5.8317

5.8187

5.8020

5.7909

5.7723

9.38%

18.22%

22.64%

27.06%

3

3,10

3,5,10

3,5,6,10

0.6602

1.3204

1.7238

1.9900

0.0941

0.1041

0.1141

0.1141

5.3. Comparisons of total real power loss and percentage reduction in all cases It is observed that total real power loss in all Cases are shown in Fig.7, in case 1 (without PV-WT based DG) the total maximum losses are 0.2897p.u MW with ZIP load and minimum losses are 0.2777p.u MW with constant load. In case 2 (with one PV-WT based DGs) the minimum losses are 0.2608p.u MW with constant load and maximum are 0.2625p.u MW with ZIP load. In case 3 (with two PV-WT based DGs) the minimum losses are 0.2369p.u MW with ZIP load and maximum are 0.2370p.u MW with constant load. In case 4 (with three PV-WT based DGs) the minimum losses are 0.2241p.u MW with ZIP load2 and maximum are 0.2246p.u MW with constant load. In case 5 (with four PV-WT based DGs) the minimum losses are 0.2113p.u MW with ZIP load and maximum are 0.2139p.u MW with constant load. Constant load

Constant load

Zip load Perecentage reduction in real power loss

Total active power loss (p.uMW)

0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 Case1

Case2

Case3

Case4

Case5

Fig.7. total real power loss in all cases with and without ZIP load

Zip load

30

25 20 15 10 5 0 Case2

Case3

Case4

Case5

Fig.8. percentage reduction in losses in all cases with and without ZIP load

The total percentage reduction losses in all Cases shown in Fig.8, It is observed that in case 2 (with one PV-WT based DG) the percentage reduction in loss is minimum 9.35% with constant load and maximum is 9.38% with variable ZIP load. In case 3 (with two PV-WT based DGs) the percentage reduction in loss is minimum 17.62% with constant load and maximum is 18.22% with ZIP load. In case 4 (with three PV-WT based DGs) the percentage reduction in loss is minimum 21.93% with constant load and maximum is 22.64% with ZIP load. In case 5 (with four PV-WT based DGs) the percentage reduction in loss is minimum 25.65% with constant load and maximum is 27.06% with ZIP load. 5.4. Power generation of Bilateral and Pool in all cases It has been also obtained the bilateral and pool electricity model, the bilateral and pool market power generation of all cases with constant load shown in Fig9 and Fig10 respectively. Similarly Fig11 and Fig12 have shown the bilateral and pool power generation in number of bus of all cases with Zip load. The bilateral transaction matrix GD

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case1 case4

6

case2 case5

case3

5 4 3 2

1 0

G2 G7 G13 G15 G16 G18 G21 G22 G23 Generator connected to number of bus Fig.9. Bilateral power generation (p.u MW) with constant load

case3

6 5 4 3 2 1

case1 case4

case2 case5

G1

G2 G7 G13 G15 G16 G18 G21 G22 G23 Generator connetceg to number of bus Fig.10. Pool power generation (p.u MW) with constant load

case3

5 4 3 2 1 0

case1 case4

7 Pool market power generation in p.u MW

Bilateral market power generation in p.u MW

case2 case5

0 G1

6

case1 case4

7 Pool market power generation in p.u MW

Bilateral power generation in p.u MW

is obtained by solving the objective function under the constraints. This transaction matrix ensures the secure bilateral transaction during the transmission loss optimization problem. The pattern of bilateral transaction matrix GD is obtained with constant load is the load bus 2 GD is .7088p.u for generator bus10, .855p.u for generator bus8 and .3888p.u for generator bus7. In load bus 7, the GD at generator bus7 is .205p.u and generator bus15 is .6126p.u. In load bus 13, the GD at generator bus 1 is .54p.u, generator bus2 is .485, generator bus 4 is .37p.u, generator bus7, 10 are same is .08123p.u, generator bus15 is .4958p.u and the maximum GD for generator bus18 is 1.6625p.u. In load bus 15, the GD at generator bus13 is 1.325p.u and generator bus20 is .64p.u. In load bus23, the GD at generator bus 3 is .9p.u, generator bus 6 is .68pu, generator bus 9 is .875 and generator bus 14 is .97p.u.

case2 case5

case3

6 5 4 3 2 1 0

G1

G2

G13 G15 G16 G18 G21 G22 G23

Generator connected to number of Bus Fig.11. Bilateral power generation (p.u MW) with Zip load

G1

G7

G13

G15

G16

G18

G21

G23

Generator connected to number of bus Fig.12. Pool power generation (p.u MW) with Zip load

The pattern of bilateral transaction matrix GD is obtained with Zip load are the load bus 1 GD for different generator bus are .485p.u at generator bus 2, .905p.u at generator bus 19 and .53p.u at generator bus 20. In load bus2, the GD at generator bus 8 is.7262p.u and 1.194p.u at generator bus 18. In load bus13 the GD of generator bus 9 is .04619p.u. In load bus 16, the GD for generator bus 7 and 9 is .625p.u and .625p.u respectively. In load bus 18, the generators bus are 6, 10, 14,15,16,18 GD are .4208p.u, .322p.u, .97p.u, .1743p.u, .5p.u, .4712pu respectively. In load bus 20 the GD are .11p.u at generator bus 18. In load bus21, the generator bus 4 and 5 GD are .37p.u and .355p.u respectively. In load bus 22 the GD is .1826p.u in generator bus 13 and the load bus 23 the number of generator bus GD obtained its .9p.u, .6013p.u, .5535p.u, .4253p.u , 1.369p.u GD with respective generator bus3,9,10,13,15.

6. Conclusion In this work the PV-WT based DG system is presented, the Weibull PDF model is used to describe the probabilistic nature of wind speed and the beta PDF model is used to describe the probabilistic nature of solar irradiance. The wind speed samples and solar irradiance samples are produced using MCS. The four different type of the Wind turbine (WT) and four PV modules are used to design the WT-PV based DG system. It presents the

Manish Kumar et al. / Procedia Technology 25 (2016) 692 – 701

comparison of optimal location and size of distributed generation for minimization of total transmission loss with constant and zip load models. The optimization problem is formulated as MNLP, under a GAMS environment. It is observed that in case of zip load the losses are more in the system without PV-WT based DG as compared to the constant load model. But when PV-WT based DGs are added in the system, there is reduction in active power loss in case of zip load as compared to the constant P, Q load. With increase in the number of PV-WT based DGs, the power loss reduces considerably. The comparison in total real power loss and percentage reduction in all Cases, it obtained all Cases the maximum total real power loss in ZIP load model and minimum for constant load model. It is obtained the percentage reduction in loss is maximum with ZIP load and minimum in constant load model. 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