Strategy for optimal location and rating of wind power generator with maximization of social welfare in double auction competitive power market

Strategy for optimal location and rating of wind power generator with maximization ofsocial welfare in double auction competitive power marketNaveen Kumar Sharma and Yog Raj Sood Citation Journal of Renewable and Sustainable Energy 6 013123 (2014) doi 10106314863089 View online httpdxdoiorg10106314863089 View Table of Contents httpscitationaiporgcontentaipjournaljrse61ver=pdfcov Published by the AIP Publishing Articles you may be interested in Optimal risk-based strategy of distributed generation-owning retailer in the day-ahead electricity market Chanceconstraint optimization approach J Renewable Sustainable Energy 6 053111 (2014) 10106314896786 Simultaneous distributed generation and capacitor placement and sizing in radial distribution system consideringreactive power market J Renewable Sustainable Energy 6 043124 (2014) 10106314893431 Imperialist competitive algorithm for optimal design of on-grid hybrid green power system integrated with a staticcompensator for reactive power management J Renewable Sustainable Energy 5 013115 (2013) 10106314790816 Robust optimization-based DC optimal power flow for managing wind generation uncertainty AIP Conf Proc 1499 31 (2012) 10106314768966 Optimal sizing of micro grid amp distributed generation units under pool electricity market J Renewable Sustainable Energy 3 053103 (2011) 10106313643268

This article is copyrighted as indicated in the article Reuse of AIP content is subject to the terms at httpscitationaiporgtermsconditions Downloaded to IP

1523102242 On Sat 11 Oct 2014 135345

Strategy for optimal location and rating of wind powergenerator with maximization of social welfare in doubleauction competitive power market

Naveen Kumar Sharmaa) and Yog Raj SoodDepartment of Electrical Engineering National Institute of Technology (NIT)Hamirpur (HP)-177005 India

(Received 2 April 2013 accepted 10 January 2014 published online 29 January 2014)

This paper presents a generalized optimal model for determining the location and

rating of wind power generation in double auction competitive power market The

optimization technique has been used with an objective to maximizing the social

welfare and profit to wind gencos company (WP-GENCO) while consider all

system constraints The performance of any electricity market is assessed by its

impact on social welfare which may be defined as ldquothe difference of the benefit of

the energy to society as measured by societyrsquos willingness to pay for its demand

and the cost of energyrdquo Each distribution company (Disco) will pay an amount to

independent system operator (ISO) for purchase the power and each generation

company (Genco) will receive an amount from the ISO to sale the power The

amount to be paid by each Disco and amount to be received by each Genco has

been determined by pay as bidding price approach The social welfare has been

then determined based on total payments and receipts The proposed approach has

been applied to modified IEEE 30-bus test system in which bidding at all generator

buses and some load buses has been introduced VC 2014 AIP Publishing LLC

[httpdxdoiorg10106314863089]

I INTRODUCTION

Energy demands are increasing rapidly requiring more energy resources to meet these

demands resulting in an increase in environmental pollution and global warming if this

demand is met from fossil fuel based thermal power plants Renewable energy sources play

a critical role in the success of deregulation of a power sector The deregulation process pri-

marily focuses on enhancing system efficiency improving service standards and developing

competitive market It has changed the traditional mission and mandates of utilities in com-

plex ways and had large impacts on environmental social and political conditions for any

particular country1 Renewable energy sources (RES) is gradually being recognized as im-

portant options in competitive electricity markets With the price of oil reaching its highest

levels and nonavailable of good quality of coal combined with the desire to reduce carbon

dioxide emissions renewable energy has become an important alternative as a power pro-

vider2 Renewable energy sources are best options for electricity production in order to

achieve goals such as carbon-dioxide emission reduction and energy independence in com-

petitive electricity market

The location of best place installation and preferable size of the RES in deregulated envi-

ronment of power sector is a complex combination optimization problem Singh et al3 have

used mixed integer nonlinear programming for optimal locations for combined fuel cell and

RES However in Ref 3 the analytic hierarchy process used to make a decision over getting

a)Author to whom correspondence should be addressed Electronic mail naveen31sharmagmailcom Tel thorn91 1972

254522 Fax thorn91 1972 223834

1941-701220146(1)01312310$3000 VC 2014 AIP Publishing LLC6 013123-1

JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY 6 013123 (2014)


1523102242 On Sat 11 Oct 2014 135345

the optimal locations of renewable energy sources Kroposi et al4 proposed optimizes the siz-

ing and placement of RES on electrical distribution feeders based on technical and economic

considerations But Refs 3 and 4 have not considered the maximization of social welfare as

objective function Optimal placement of wind turbines which maximizes the system social

welfare by using step controlled primal dual interior point method is presented in Ref 5 but

this paper considers optimal location of RES in radial distribution system only

References 6 and 7 have proposed optimal placement of renewable energy sources to

minimize fuel and emission costs of overall system The multiobjective bees algorithm has

been used to minimize simultaneously fuel cost and emission of thermal units by changing

location and varying sizes of solar farm with security constraints of power system6 Ant col-

ony based model has been proposed for optimal location of distributed generator (DG) in a

distribution network as the minimization of the investment cost of DG and total operation

costs of the system in Refs 8 and 9 Musi et al10 proposed model for optimal planning of

renewable energy integrated electricity generation with CO2 reduction by mixed integer lin-

ear programming Nara and Hayashi11 have been presented tabu search application for find-

ing the optimal allocation of DGs from a viewpoint of loss minimization Fuzzy-Genetic

Algorithm (GA) based method has been presented to resolve dispersed generator placement

for distribution systems12 Borges and Falcao13 have proposed a model for optimal distrib-

uted generation allocation for reliability losses and voltage improvement However all

references6ndash13 have used only in classical integrated model of power system

Reference 14 have proposed optimal allocation and sizing distributed generation in deregu-

lated electricity market assuming quadratic characteristics of such distributed generator Porkar

et al15 have proposed optimal planning frameworks for implementing distributed generation in

deregulated power sector without considering wind power generate company (WP-GENCO)

profit

In this paper an approach has been proposed in order to determine the optimal location

and rating of wind power generation in deregulated environment of power sector with an

objective to maximized social welfare minimized generation cost of wind power generation

(WPG) in double auction competitive power market In double auction bidding models both

the Gencos and Discos are allowed to offer and bid their prices to independent system opera-

tor (ISO) The Discos are required to pay an amount in order to purchase the power whereas

Gencos received the amount for sailing their power to discos The amount to be paid by

each disco and amount to be received by each Genco has been determined by actual bidding

price approach The maximization of WP-GENCO profit reduction in marginal price and

total system real power losses are also achieved with the proposed approach MATLAB pro-

gramming codes for the proposed technique have been developed and incorporated for the

simulation purpose In this paper the MATPOWER16 m-files are modified with adding the pro-

posed optimization codes to solve the problem In the proposed approach this Optimal Power

Flow (OPF) has been run several times for each and every possible location and sizing of WPG

and best location will be decided at which objective function gets maximum value

II MATHEMATICAL FORMULATION

In this section the mathematical formulation of proposed approach with maximization of

social welfare and minimization of wind power generation cost with optimal location and size

of WPG In the considered power market model bulk loads as well as retailers are required to

bid their maximum demand and price function All generators are also required to bid their gen-

eration cost function along with their maximum generation

The Gencos participating in the pool bid their cost function and maximum generation

which they want to deliver to the pool Similarly loads bid their price function as well as their

maximum demand which they are willing to take from the pool17 After optimization of social

welfare the demand as well generation at all the buses are known Let the vector of pool real

power demand is

013123-2 N K Sharma and Y R Sood J Renewable Sustainable Energy 6 013123 (2014)


1523102242 On Sat 11 Oct 2014 135345

Pdp frac14 Pdpj j frac14 1 2 3 nd

n o(1)

and vector of pool real power generation is

Pgp frac14 Pgpi i frac14 1 2 3 ng

(2)

Consider a system having total nb number of buses ng number of generators and nd num-

ber of loads Let the generation cost curve bid to the pool by generator at bus i be denoted by

CiethPgpi THORN Let the worth function (which is also called benefit function) for load that is price de-

pendent be BjethPdpj THORN It represents the price the load is willing to pay to purchase an amount of

power ethPdpj THORN

In a competitive power market when the energy demand is price elastic that is ldquosensitive

to the energy pricerdquo in which both Gencos amp Discos are allowed to bid their prices then the

social welfare is given by Eq (3) This type of bidding is called double auction bidding It can

be shown that a perfect market has maximum social welfare (SW) Real markets always operate

at lower levels of social welfare The difference in social welfare between a perfect market and

a real market is a measure of the efficiency of the real market

For an elastic load (or in double auction power market) the social welfare is evaluated as

SWfrac14Xnd

jfrac141

BjethPdjTHORN Xng

ifrac141

CiethPgiTHORN

8lt

9= (3)

where BjethPdjTHORN is the price (in dollar) the consumer rsquojrsquo willing to pay to ISO for purchasing Pdj

MW of power

Mathematically the objective function ethFTHORN is to maximize the social welfare and minimize

the generation cost of WPG So the objective function is given as

F frac14 maxXnd

jfrac141

BjethPdpj THORN

Xng

ifrac141

Ci Pgpi

CWPG PWk

8lt

9= $=h (4)

where CWPG is cost of $MWh for WP-GENCO and PW is the size of WPG at bus k In this

paper we have taken 50$MWh as wind power generation cost CWPGeth THORN for WP-GENCO after

considering carbon credit1819

The objective function Eq (4) is maximized subject to the following transmission network

constraints

bull The power flow equation of the power network

gethVTHORN frac14 0 (5)

where

gethVTHORN frac14

PiethVTHORN Pneti g

QiethVTHORN Qneti g

For each PQ bus i

PmethVTHORN Pnetm g

For each PV bus

m not including

the ref bus

8gtgtgtgtltgtgtgtgt

where Pi and Qi are respectively calculated real and reactive powers for PQ bus i Pneti and

Qneti are respectively specified real and reactive power for PQ bus iPm and Pnet

m are respec-

tively calculated and specified real power for PV bus m Vand are voltage magnitude and

phase angles at different buses



1523102242 On Sat 11 Oct 2014 135345

bull The inequality constraint on real power generation Pgi at PV buses

Pgmini Pgi Pgmax

i (6)

where Pgmini and Pgmax

i are respectively minimum and maximum values of real power gen-

eration allowed at generator bus ibull The inequality constraint on reactive power generation Qgi at PV bus

Qgmini Qgi Qgmax

i (7)

where Qgmini and Qgmax

i are respectively minimum and maximum value of reactive power at

PV bus ibull The inequality constraint on voltage magnitude V of each PQ bus

Vmini Vi Vmax

i (8)

where Vmini and Vmax

i are respectively minimum and maximum voltage at bus ibull MVA flow limit on transmission line

MVAfij MVAf maxij (9)

where MVAf maxij is the maximum rating of transmission line connecting bus i and j

With the offer characteristics of all pool generators and bidding characteristics of all pool

demands the optimization of objective function Eq (4) has been carried out with satisfying all

constraints Eqs (5)ndash(9) along with generation offers and demand bidding constraints which are

the maximum limits of offers as well as demand bids Social welfare is a difference of the ben-

efit of the energy to society as measured by societyrsquos willingness to pay for its demand and the

cost of energy The marginal prices at all system buses are also determined by this optimization

process Marginal pricing is also called nodal price or locational marginal price (LMP) of elec-

tricity plays an important role in a competitive power market2021

Let kk is the LMP at bus k So Profitk is to WP-GENCO for locating PWk MW of wind

power at bus k Hence

Profitk frac14 kk PWk CWPG PWk (10)

A Step by step algorithm and flow chart of proposed approach

The flow chart of the proposed approach is given in Figure 1 In this figure PW is the rat-

ing of WPG in MW F is value of objective function Fwithout is value of objective function

without WPG in the system and FwithethPWTHORN is value of objective function with PW rating of

WPG in the system Let n is the total number of buses in the system The step by step algo-

rithm of the proposed deregulated model of optimal location of WPG is given as follows

Step 1 Read all system data

Step 2 Set the initial rating of WPG to zero (PW frac14 0)

Step 3 Run OPF without considering WPG in the network and find social welfare and

objective function F put Fwithout frac14 F

Step 4 Set Bus count NB frac14 1 and PW frac14 0

Step 5 Increment rating of WPG by 1 ie PW frac14 PW thorn 1

Step 6 Locate WPG of PW rating (MW) at Bus NB

Step 7 Run OPF and determining the social welfare objective function determine

F frac14 FwithethPWTHORNStep 8 Check the rating of PW reaches its maximum value or not If this is true go to

next step otherwise go to step (5)

Step 9 Determine the MFwithfrac14maximum of FwithethPWTHORN



1523102242 On Sat 11 Oct 2014 135345

Step 10 If NB is the less than n increment bus counts NB frac14 NB thorn 1 and set again

(PW frac14 1) go to step (5) otherwise go to next step

Step 11 Determine optimal value of objective function and optimal location of WPG

Step 12 Prepare a priority order as mentioned in Table VI

Step 13 Print all results

Step 14 END

III SIMULATION ANALYSIS

The proposed approach for optimal location and size of WPG by a WP-GENCO (Wind

Genoco Company) has been tested and analyzed on a modified IEEE 30-bus test system The

data and single line diagram of this system is given in Refs 16 and 21 In this system all the

FIG 1 Flow chart of proposed approach



1523102242 On Sat 11 Oct 2014 135345

six generators are assumed to bid the quadratic cost characteristics as given in Eq (11) with

the generators bidding coefficient as shown in Table I

Ci Pgieth THORN frac14 ai Pgieth THORN2 thorn bi Pgieth THORN thorn ci (11)

Similarly load at seven demand buses are also assumed to bid the quadratic cost character-

istics as given in Eq (12) with the demands bidding coefficient shown in Table II

Bi Pdieth THORN frac14 ai Pdieth THORN2 thorn bi Pdieth THORN thorn ci (12)

By the proposed approach first optimization has been performed without considering WPG

in the network and determines the optimal values of objective function and social welfare

After that the optimal size and optimal location of WPG is determined with the proposed

approach The optimal result is given in Table III and the corresponding optimal generation pat-

terns and load patterns are given in Tables IV and V respectively

TABLE I Generator bidding coefficients

S No Bus number

Generator bidding coefficientsReal power (Pgmax)

generation limitsa b c

1 1 00700 55 0 80

2 2 00700 55 0 80

3 22 00083 38 0 50

4 27 00083 38 0 50

5 23 00250 30 0 30

6 13 00700 55 0 40

TABLE II Demand bidding coefficients

S No Bus number

Demand bidding coefficientsReal power (Pdmax)

demand limitsa b c

1 4 000533 100 0 152

2 7 000889 90 0 456

3 8 000741 83 0 600

4 12 000533 100 0 224

5 17 000889 90 0 180

6 21 000741 83 0 350

7 30 000533 100 0 212

TABLE III Optimal size and location of WPG

S No Items Optimal value

1 Social welfare without WPG ($h) 368870

2 Social welfare with WPG ($h) 985241

3 Generation cost of WPG ($h) 460000

4 Optimal location bus number 8

5 Optimal size (MW) 92

6 Objective function ($h) 525241



1523102242 On Sat 11 Oct 2014 135345

TABLE IV Optimal Generation patterns with and without WPG

S No Bus number Without WPG (MW)

With WPG of 92 MW

at bus number 8 (MW)

1 1 6082 2867

2 2 6513 3081

3 22 2873 2519

4 27 5500 5500

5 23 2999 2976

6 13 4000 4000

7 8 ndash 9200

TABLE V Optimal Load patterns at the buses where the real demand bidding has been done

S No Bus number

Allowed bid

(without WPG) (MW)

Allowed bid (with WPG of 92 MW

at bus number 8) (MW)

1 4 152 152

2 7 456 456

3 8 413 600

4 12 224 224

5 17 180 180

6 21 306 350

7 30 212 212

FIG 2 Marginal price with and without optimal location of WPG at bus no 8

TABLE VI Optimal location based on maximized objective function as well as profit to WP-GENCO

Priority wise

options based

on objective

function

Objective

function

($h)

Optimal location

of WPG at

bus number

Optimal rating

of WPG

(MW)

Social

welfare

($h)

Generation

cost of WPG

($h)

Marginal

pricing of

WPG

($MWh)

Revenue

received by

WPG ($h)

Profit to

WP-GENCO

($h)

1 525241 8 92 985241 4600 5899 542699 82699

2 522882 4 173 13 87882 8650 5004 865606 606

3 519420 10 128 11 59420 6400 5365 686694 46694

4 514176 6 167 13 49176 8350 5011 836754 1753

5 513637 9 125 11 38637 6250 5022 627725 2725

6 511805 3 161 13 16805 8050 5241 843753 38753

7 507652 2 161 13 12652 8050 5024 808896 3896



1523102242 On Sat 11 Oct 2014 135345

Marginal price are found lower with optimal locating WPG for pool model The graphical

representation of reduction in marginal pricing with optimal location of WPG is shown in

Figure 2 The total real power losses without WPG is 486 MW whereas with WPG is

353 MW Hence there is considerably the reduction in real power losses by optimal placement

of WPG by this proposed approach

FIG 3 Priority wise preference based on maximization of objective function

FIG 4 Priority wise preference based on maximization of social welfare

FIG 5 Priority wise preference based on minimization of cost of WPG



1523102242 On Sat 11 Oct 2014 135345

The priority wise optimal location of WPG based on objective function is given in

Table VI As is seen in this table the first option presents the location of 92 MW of WPG at

bus number 8 As per this option the profit to WP-GENCO is maximum but social welfare is

minimum Therefore if we want to get maximum profit of WP-GENCO this point is most suit-

able for the location of WPG

The second option presents the optimal location of 173 MW WPG at bus number 4 As per

this option the social welfare is maximum whereas profit of WP-GENCO is minimum That is

why it is the best location of WPG for getting maximum social welfare However if due to

climate condition (nonavailability of sufficient wind speed) at bus number 8 and bus number 4

The other options in priority table may also be considered for the locations

FIG 6 Priority wise preference based on maximization of profit to WP-GENCO

FIG 7 Modified IEEE 30-bus system showing the optimal location of WPG for all seven priorities



1523102242 On Sat 11 Oct 2014 135345

As per third option the wind generator of 128 MW may be located at bus number 10 It provides

the second best option for profit of WP-GENCO As per fourth option the wind generator of

167 MW may be located at bus number 6 It provides the second best option for social welfare

In case wind speedweather condition are not in favor at first priority than WPG may be

located second preferences as so on The preferences order may be written based on maximiza-

tion of objective function is given in Table VI and Figure 3 the maximum of social welfare as

shown in Figure 4 the minimization of cost of WPG as shown in Figure 5 the maximum of

profit of WPG as shown in Figure 6

From Table VI it is clear that there is a considerable improvement in social welfare as

compared with without WPG in all priority options The seven priorities for optimal location of

WPG have been mentioned in Table VI and are shown in Figure 7

IV CONCLUSIONS

In this paper an optimal approach for location and rating of wind generator as WPG with maxi-

mization of social welfare and minimization of wind power generation cost has been presented The

proposed approach has been applied to modified IEEE 30-bus test system in deregulated environ-

ment of power sector The WP-GENCO profit and effect of WPG on system real power losses and

marginal pricing of electricity have been also investigated and presented The optimal location of

WPG has been determined by making priority order which is based on value of objective function

ACKNOWLEDGMENTS

The authors wish to thank anonymous referees who reviewed this paper and gave their valuable

comments and helpful suggestions Moreover the first author would also like to thank his mother

Smt Nirmala Sharma and father Shri Govind Prasad Sharma for their continuous support and belief

in him during difficult times

1Y R Sood N P Padhy and H O Gupta IEEE Trans Power Syst 17 870 (2002)2Y R Sood and R Singh Renewable Energy 35 1828 (2010)3A K Singh and S K Parida ldquoCombined optimal placement of solar wind and fuel cell based DGs using AHPrdquo inProceedings of the World Renewable Energy Congress Sweden (2011) pp 3113ndash3120

4B Kroposki P K Sen and K Malmedal ldquoOptimum sizing and placement of distributed and renewable energy sourcesin electric power distribution systemsrdquo in IEEE Industry Applications Society Annual Meeting (2009) pp 1ndash10

5G Mokryani P Siano and A Piccolo ldquoSocial welfare maximization for optimal allocation of wind turbines in distribution sys-temsrdquo in Proceedings of the 11th International Conference on Electrical Power Quality and Utilisation (EPQU) (2011) pp 1ndash6

6N Leeprechanon and P Phonrattanasak ldquoOptimal placement of solar farm on the power system networkrdquo inProceedings of the Second TSME International Conference on Mechanical Engineering Krabi (2011) pp 1ndash7

7A Kaabeche M Belhamel and R Ibtiouen ldquoOptimal sizing method for stand-alone hybrid PVwind power generationsystemrdquo Revue des Energies Renouvelables (SMEErsquo10) Bou Ismail Tipaza (2010) pp 205ndash213

8H Falaghi and M R Haghifam ldquoACO based algorithm for distributed generation sources allocation and sizing in distri-bution systemsrdquo in Proceedings of the IEEE Power Tech Lausanne (2007) pp 555ndash560

9J F Gomez H M Khodr P M De Oliveira L Ocque J M Yusta R Villasana and A J Urdaneta IEEE TransPower Syst 19 996 (2004)

10Z A Muis H Hashim Z A Manan F M Taha and P L Douglas Renewable Energy 35 2562 (2010)11K Nara Y Hayashi K Ikeda and T Ashizawa ldquoApplication of tabu search to optimal placement of distributed gener-

atorsrdquo in Proceedings of the IEEE Power Engineering Society Winter Meeting (2001) pp 918ndash92312K H Kim Y J Lee S B Rhee S K Lee and S K You ldquoDispersed generator placement using fuzzy-GA in distribu-

tion systemsrdquo IEEE Power Engineering Society Summer Meeting Chicago (2002) pp 1148ndash115313C L T Borges and D M Falcao Int J Electr Power Energy Syst 28 413 (2006)14D Gautam and N Mithulananthan Electr Power Syst Res 77 1627 (2007)15S Porkar P Poure A Abbaspour-Tehrani-fard and S Saadate Electr Power Syst Res 80 828 (2010)16R D Zimmerman C E Murillo-Sanchez and D Gan MATPOWER A matlab power system simulation package

(2006) See httppserccornelledumatpower17Y R Sood N P Padhy and H O Gupta Electr Power Syst Res 77 574 (2007)18See http wwwwindforce-managementcom for Wind Force Newsletter - Nov Edition 2011 - Wind power project

Enabling High Efficiency and Reliable Wind Power Projects (2011)19See http wwwcercindgovin2012ordersRE_35_2012pdf for central electricity regulatory commission (CERC)

terms and conditions for tariff determination from renewable energy sources regulations (2012) [Accessed March 2012]20L L Loi Power System Restructuring and Deregulation (John Wiley and Sons Ltd New York 2001)21M A Pai Computer Techniques in Power System Analysis (Tata McGraw- Hill Publishing Company Limited New

Delhi 1980)



1523102242 On Sat 11 Oct 2014 135345

Strategy for optimal location and rating of wind powergenerator with maximization of social welfare in doubleauction competitive power market

Naveen Kumar Sharmaa) and Yog Raj SoodDepartment of Electrical Engineering National Institute of Technology (NIT)Hamirpur (HP)-177005 India

(Received 2 April 2013 accepted 10 January 2014 published online 29 January 2014)

This paper presents a generalized optimal model for determining the location and

rating of wind power generation in double auction competitive power market The

optimization technique has been used with an objective to maximizing the social

welfare and profit to wind gencos company (WP-GENCO) while consider all

system constraints The performance of any electricity market is assessed by its

impact on social welfare which may be defined as ldquothe difference of the benefit of

the energy to society as measured by societyrsquos willingness to pay for its demand

and the cost of energyrdquo Each distribution company (Disco) will pay an amount to

independent system operator (ISO) for purchase the power and each generation

company (Genco) will receive an amount from the ISO to sale the power The

amount to be paid by each Disco and amount to be received by each Genco has

been determined by pay as bidding price approach The social welfare has been

then determined based on total payments and receipts The proposed approach has

been applied to modified IEEE 30-bus test system in which bidding at all generator

buses and some load buses has been introduced VC 2014 AIP Publishing LLC

[httpdxdoiorg10106314863089]

I INTRODUCTION

Energy demands are increasing rapidly requiring more energy resources to meet these

demands resulting in an increase in environmental pollution and global warming if this

demand is met from fossil fuel based thermal power plants Renewable energy sources play

a critical role in the success of deregulation of a power sector The deregulation process pri-

marily focuses on enhancing system efficiency improving service standards and developing

competitive market It has changed the traditional mission and mandates of utilities in com-

plex ways and had large impacts on environmental social and political conditions for any

particular country1 Renewable energy sources (RES) is gradually being recognized as im-

portant options in competitive electricity markets With the price of oil reaching its highest

levels and nonavailable of good quality of coal combined with the desire to reduce carbon

dioxide emissions renewable energy has become an important alternative as a power pro-

vider2 Renewable energy sources are best options for electricity production in order to

achieve goals such as carbon-dioxide emission reduction and energy independence in com-

petitive electricity market

The location of best place installation and preferable size of the RES in deregulated envi-

ronment of power sector is a complex combination optimization problem Singh et al3 have

used mixed integer nonlinear programming for optimal locations for combined fuel cell and

RES However in Ref 3 the analytic hierarchy process used to make a decision over getting

a)Author to whom correspondence should be addressed Electronic mail naveen31sharmagmailcom Tel thorn91 1972

254522 Fax thorn91 1972 223834

1941-701220146(1)01312310$3000 VC 2014 AIP Publishing LLC6 013123-1

JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY 6 013123 (2014)


1523102242 On Sat 11 Oct 2014 135345

























profit


























power demand is



1523102242 On Sat 11 Oct 2014 135345


n o(1)



(2)





power ethPdpj THORN








SWfrac14Xnd

jfrac141

BjethPdjTHORN Xng

ifrac141

CiethPgiTHORN

8lt

9= (3)


MW of power



F frac14 maxXnd

jfrac141

BjethPdpj THORN

Xng

ifrac141

Ci Pgpi

CWPG PWk

8lt

9= $=h (4)





constraints



where

gethVTHORN frac14

PiethVTHORN Pneti g

QiethVTHORN Qneti g

For each PQ bus i

PmethVTHORN Pnetm g

For each PV bus

m not including

the ref bus

8gtgtgtgtltgtgtgtgt



m are respec-





1523102242 On Sat 11 Oct 2014 135345


Pgmini Pgi Pgmax

i (6)




Qgmini Qgi Qgmax

i (7)




Vmini Vi Vmax

i (8)



































1523102242 On Sat 11 Oct 2014 135345






Step 14 END








1523102242 On Sat 11 Oct 2014 135345













S No Bus number



1 1 00700 55 0 80

2 2 00700 55 0 80

3 22 00083 38 0 50

4 27 00083 38 0 50

5 23 00250 30 0 30

6 13 00700 55 0 40


S No Bus number


demand limitsa b c

1 4 000533 100 0 152

2 7 000889 90 0 456

3 8 000741 83 0 600

4 12 000533 100 0 224

5 17 000889 90 0 180

6 21 000741 83 0 350

7 30 000533 100 0 212











1523102242 On Sat 11 Oct 2014 135345



With WPG of 92 MW


1 1 6082 2867

2 2 6513 3081

3 22 2873 2519

4 27 5500 5500

5 23 2999 2976

6 13 4000 4000

7 8 ndash 9200


S No Bus number

Allowed bid

(without WPG) (MW)



1 4 152 152

2 7 456 456

3 8 413 600

4 12 224 224

5 17 180 180

6 21 306 350

7 30 212 212



Priority wise

options based

on objective

function

Objective

function

($h)

Optimal location

of WPG at

bus number

Optimal rating

of WPG

(MW)

Social

welfare

($h)

Generation

cost of WPG

($h)

Marginal

pricing of

WPG

($MWh)

Revenue

received by

WPG ($h)

Profit to

WP-GENCO

($h)

1 525241 8 92 985241 4600 5899 542699 82699

2 522882 4 173 13 87882 8650 5004 865606 606

3 519420 10 128 11 59420 6400 5365 686694 46694

4 514176 6 167 13 49176 8350 5011 836754 1753

5 513637 9 125 11 38637 6250 5022 627725 2725

6 511805 3 161 13 16805 8050 5241 843753 38753

7 507652 2 161 13 12652 8050 5024 808896 3896



1523102242 On Sat 11 Oct 2014 135345











1523102242 On Sat 11 Oct 2014 135345















1523102242 On Sat 11 Oct 2014 135345












IV CONCLUSIONS







ACKNOWLEDGMENTS


















Delhi 1980)



1523102242 On Sat 11 Oct 2014 135345

























profit


























power demand is



1523102242 On Sat 11 Oct 2014 135345


n o(1)



(2)





power ethPdpj THORN








SWfrac14Xnd

jfrac141

BjethPdjTHORN Xng

ifrac141

CiethPgiTHORN

8lt

9= (3)


MW of power



F frac14 maxXnd

jfrac141

BjethPdpj THORN

Xng

ifrac141

Ci Pgpi

CWPG PWk

8lt

9= $=h (4)





constraints



where

gethVTHORN frac14

PiethVTHORN Pneti g

QiethVTHORN Qneti g

For each PQ bus i

PmethVTHORN Pnetm g

For each PV bus

m not including

the ref bus

8gtgtgtgtltgtgtgtgt



m are respec-





1523102242 On Sat 11 Oct 2014 135345


Pgmini Pgi Pgmax

i (6)




Qgmini Qgi Qgmax

i (7)




Vmini Vi Vmax

i (8)



































1523102242 On Sat 11 Oct 2014 135345






Step 14 END








1523102242 On Sat 11 Oct 2014 135345













S No Bus number



1 1 00700 55 0 80

2 2 00700 55 0 80

3 22 00083 38 0 50

4 27 00083 38 0 50

5 23 00250 30 0 30

6 13 00700 55 0 40


S No Bus number


demand limitsa b c

1 4 000533 100 0 152

2 7 000889 90 0 456

3 8 000741 83 0 600

4 12 000533 100 0 224

5 17 000889 90 0 180

6 21 000741 83 0 350

7 30 000533 100 0 212











1523102242 On Sat 11 Oct 2014 135345



With WPG of 92 MW


1 1 6082 2867

2 2 6513 3081

3 22 2873 2519

4 27 5500 5500

5 23 2999 2976

6 13 4000 4000

7 8 ndash 9200


S No Bus number

Allowed bid

(without WPG) (MW)



1 4 152 152

2 7 456 456

3 8 413 600

4 12 224 224

5 17 180 180

6 21 306 350

7 30 212 212



Priority wise

options based

on objective

function

Objective

function

($h)

Optimal location

of WPG at

bus number

Optimal rating

of WPG

(MW)

Social

welfare

($h)

Generation

cost of WPG

($h)

Marginal

pricing of

WPG

($MWh)

Revenue

received by

WPG ($h)

Profit to

WP-GENCO

($h)

1 525241 8 92 985241 4600 5899 542699 82699

2 522882 4 173 13 87882 8650 5004 865606 606

3 519420 10 128 11 59420 6400 5365 686694 46694

4 514176 6 167 13 49176 8350 5011 836754 1753

5 513637 9 125 11 38637 6250 5022 627725 2725

6 511805 3 161 13 16805 8050 5241 843753 38753

7 507652 2 161 13 12652 8050 5024 808896 3896



1523102242 On Sat 11 Oct 2014 135345











1523102242 On Sat 11 Oct 2014 135345















1523102242 On Sat 11 Oct 2014 135345












IV CONCLUSIONS







ACKNOWLEDGMENTS


















Delhi 1980)



1523102242 On Sat 11 Oct 2014 135345


n o(1)



(2)





power ethPdpj THORN








SWfrac14Xnd

jfrac141

BjethPdjTHORN Xng

ifrac141

CiethPgiTHORN

8lt

9= (3)


MW of power



F frac14 maxXnd

jfrac141

BjethPdpj THORN

Xng

ifrac141

Ci Pgpi

CWPG PWk

8lt

9= $=h (4)





constraints



where

gethVTHORN frac14

PiethVTHORN Pneti g

QiethVTHORN Qneti g

For each PQ bus i

PmethVTHORN Pnetm g

For each PV bus

m not including

the ref bus

8gtgtgtgtltgtgtgtgt



m are respec-





1523102242 On Sat 11 Oct 2014 135345


Pgmini Pgi Pgmax

i (6)




Qgmini Qgi Qgmax

i (7)




Vmini Vi Vmax

i (8)



































1523102242 On Sat 11 Oct 2014 135345






Step 14 END








1523102242 On Sat 11 Oct 2014 135345













S No Bus number



1 1 00700 55 0 80

2 2 00700 55 0 80

3 22 00083 38 0 50

4 27 00083 38 0 50

5 23 00250 30 0 30

6 13 00700 55 0 40


S No Bus number


demand limitsa b c

1 4 000533 100 0 152

2 7 000889 90 0 456

3 8 000741 83 0 600

4 12 000533 100 0 224

5 17 000889 90 0 180

6 21 000741 83 0 350

7 30 000533 100 0 212











1523102242 On Sat 11 Oct 2014 135345



With WPG of 92 MW


1 1 6082 2867

2 2 6513 3081

3 22 2873 2519

4 27 5500 5500

5 23 2999 2976

6 13 4000 4000

7 8 ndash 9200


S No Bus number

Allowed bid

(without WPG) (MW)



1 4 152 152

2 7 456 456

3 8 413 600

4 12 224 224

5 17 180 180

6 21 306 350

7 30 212 212



Priority wise

options based

on objective

function

Objective

function

($h)

Optimal location

of WPG at

bus number

Optimal rating

of WPG

(MW)

Social

welfare

($h)

Generation

cost of WPG

($h)

Marginal

pricing of

WPG

($MWh)

Revenue

received by

WPG ($h)

Profit to

WP-GENCO

($h)

1 525241 8 92 985241 4600 5899 542699 82699

2 522882 4 173 13 87882 8650 5004 865606 606

3 519420 10 128 11 59420 6400 5365 686694 46694

4 514176 6 167 13 49176 8350 5011 836754 1753

5 513637 9 125 11 38637 6250 5022 627725 2725

6 511805 3 161 13 16805 8050 5241 843753 38753

7 507652 2 161 13 12652 8050 5024 808896 3896



1523102242 On Sat 11 Oct 2014 135345











1523102242 On Sat 11 Oct 2014 135345















1523102242 On Sat 11 Oct 2014 135345












IV CONCLUSIONS







ACKNOWLEDGMENTS


















Delhi 1980)



1523102242 On Sat 11 Oct 2014 135345


Pgmini Pgi Pgmax

i (6)




Qgmini Qgi Qgmax

i (7)




Vmini Vi Vmax

i (8)



































1523102242 On Sat 11 Oct 2014 135345






Step 14 END








1523102242 On Sat 11 Oct 2014 135345













S No Bus number



1 1 00700 55 0 80

2 2 00700 55 0 80

3 22 00083 38 0 50

4 27 00083 38 0 50

5 23 00250 30 0 30

6 13 00700 55 0 40


S No Bus number


demand limitsa b c

1 4 000533 100 0 152

2 7 000889 90 0 456

3 8 000741 83 0 600

4 12 000533 100 0 224

5 17 000889 90 0 180

6 21 000741 83 0 350

7 30 000533 100 0 212











1523102242 On Sat 11 Oct 2014 135345



With WPG of 92 MW


1 1 6082 2867

2 2 6513 3081

3 22 2873 2519

4 27 5500 5500

5 23 2999 2976

6 13 4000 4000

7 8 ndash 9200


S No Bus number

Allowed bid

(without WPG) (MW)



1 4 152 152

2 7 456 456

3 8 413 600

4 12 224 224

5 17 180 180

6 21 306 350

7 30 212 212



Priority wise

options based

on objective

function

Objective

function

($h)

Optimal location

of WPG at

bus number

Optimal rating

of WPG

(MW)

Social

welfare

($h)

Generation

cost of WPG

($h)

Marginal

pricing of

WPG

($MWh)

Revenue

received by

WPG ($h)

Profit to

WP-GENCO

($h)

1 525241 8 92 985241 4600 5899 542699 82699

2 522882 4 173 13 87882 8650 5004 865606 606

3 519420 10 128 11 59420 6400 5365 686694 46694

4 514176 6 167 13 49176 8350 5011 836754 1753

5 513637 9 125 11 38637 6250 5022 627725 2725

6 511805 3 161 13 16805 8050 5241 843753 38753

7 507652 2 161 13 12652 8050 5024 808896 3896



1523102242 On Sat 11 Oct 2014 135345











1523102242 On Sat 11 Oct 2014 135345















1523102242 On Sat 11 Oct 2014 135345












IV CONCLUSIONS







ACKNOWLEDGMENTS


















Delhi 1980)



1523102242 On Sat 11 Oct 2014 135345






Step 14 END








1523102242 On Sat 11 Oct 2014 135345













S No Bus number



1 1 00700 55 0 80

2 2 00700 55 0 80

3 22 00083 38 0 50

4 27 00083 38 0 50

5 23 00250 30 0 30

6 13 00700 55 0 40


S No Bus number


demand limitsa b c

1 4 000533 100 0 152

2 7 000889 90 0 456

3 8 000741 83 0 600

4 12 000533 100 0 224

5 17 000889 90 0 180

6 21 000741 83 0 350

7 30 000533 100 0 212











1523102242 On Sat 11 Oct 2014 135345



With WPG of 92 MW


1 1 6082 2867

2 2 6513 3081

3 22 2873 2519

4 27 5500 5500

5 23 2999 2976

6 13 4000 4000

7 8 ndash 9200


S No Bus number

Allowed bid

(without WPG) (MW)



1 4 152 152

2 7 456 456

3 8 413 600

4 12 224 224

5 17 180 180

6 21 306 350

7 30 212 212



Priority wise

options based

on objective

function

Objective

function

($h)

Optimal location

of WPG at

bus number

Optimal rating

of WPG

(MW)

Social

welfare

($h)

Generation

cost of WPG

($h)

Marginal

pricing of

WPG

($MWh)

Revenue

received by

WPG ($h)

Profit to

WP-GENCO

($h)

1 525241 8 92 985241 4600 5899 542699 82699

2 522882 4 173 13 87882 8650 5004 865606 606

3 519420 10 128 11 59420 6400 5365 686694 46694

4 514176 6 167 13 49176 8350 5011 836754 1753

5 513637 9 125 11 38637 6250 5022 627725 2725

6 511805 3 161 13 16805 8050 5241 843753 38753

7 507652 2 161 13 12652 8050 5024 808896 3896



1523102242 On Sat 11 Oct 2014 135345











1523102242 On Sat 11 Oct 2014 135345















1523102242 On Sat 11 Oct 2014 135345












IV CONCLUSIONS







ACKNOWLEDGMENTS


















Delhi 1980)



1523102242 On Sat 11 Oct 2014 135345













S No Bus number



1 1 00700 55 0 80

2 2 00700 55 0 80

3 22 00083 38 0 50

4 27 00083 38 0 50

5 23 00250 30 0 30

6 13 00700 55 0 40


S No Bus number


demand limitsa b c

1 4 000533 100 0 152

2 7 000889 90 0 456

3 8 000741 83 0 600

4 12 000533 100 0 224

5 17 000889 90 0 180

6 21 000741 83 0 350

7 30 000533 100 0 212











1523102242 On Sat 11 Oct 2014 135345



With WPG of 92 MW


1 1 6082 2867

2 2 6513 3081

3 22 2873 2519

4 27 5500 5500

5 23 2999 2976

6 13 4000 4000

7 8 ndash 9200


S No Bus number

Allowed bid

(without WPG) (MW)



1 4 152 152

2 7 456 456

3 8 413 600

4 12 224 224

5 17 180 180

6 21 306 350

7 30 212 212



Priority wise

options based

on objective

function

Objective

function

($h)

Optimal location

of WPG at

bus number

Optimal rating

of WPG

(MW)

Social

welfare

($h)

Generation

cost of WPG

($h)

Marginal

pricing of

WPG

($MWh)

Revenue

received by

WPG ($h)

Profit to

WP-GENCO

($h)

1 525241 8 92 985241 4600 5899 542699 82699

2 522882 4 173 13 87882 8650 5004 865606 606

3 519420 10 128 11 59420 6400 5365 686694 46694

4 514176 6 167 13 49176 8350 5011 836754 1753

5 513637 9 125 11 38637 6250 5022 627725 2725

6 511805 3 161 13 16805 8050 5241 843753 38753

7 507652 2 161 13 12652 8050 5024 808896 3896



1523102242 On Sat 11 Oct 2014 135345











1523102242 On Sat 11 Oct 2014 135345















1523102242 On Sat 11 Oct 2014 135345












IV CONCLUSIONS







ACKNOWLEDGMENTS


















Delhi 1980)



1523102242 On Sat 11 Oct 2014 135345



With WPG of 92 MW


1 1 6082 2867

2 2 6513 3081

3 22 2873 2519

4 27 5500 5500

5 23 2999 2976

6 13 4000 4000

7 8 ndash 9200


S No Bus number

Allowed bid

(without WPG) (MW)



1 4 152 152

2 7 456 456

3 8 413 600

4 12 224 224

5 17 180 180

6 21 306 350

7 30 212 212



Priority wise

options based

on objective

function

Objective

function

($h)

Optimal location

of WPG at

bus number

Optimal rating

of WPG

(MW)

Social

welfare

($h)

Generation

cost of WPG

($h)

Marginal

pricing of

WPG

($MWh)

Revenue

received by

WPG ($h)

Profit to

WP-GENCO

($h)

1 525241 8 92 985241 4600 5899 542699 82699

2 522882 4 173 13 87882 8650 5004 865606 606

3 519420 10 128 11 59420 6400 5365 686694 46694

4 514176 6 167 13 49176 8350 5011 836754 1753

5 513637 9 125 11 38637 6250 5022 627725 2725

6 511805 3 161 13 16805 8050 5241 843753 38753

7 507652 2 161 13 12652 8050 5024 808896 3896



1523102242 On Sat 11 Oct 2014 135345











1523102242 On Sat 11 Oct 2014 135345















1523102242 On Sat 11 Oct 2014 135345












IV CONCLUSIONS







ACKNOWLEDGMENTS


















Delhi 1980)



1523102242 On Sat 11 Oct 2014 135345











1523102242 On Sat 11 Oct 2014 135345















1523102242 On Sat 11 Oct 2014 135345












IV CONCLUSIONS







ACKNOWLEDGMENTS


















Delhi 1980)



1523102242 On Sat 11 Oct 2014 135345















1523102242 On Sat 11 Oct 2014 135345












IV CONCLUSIONS







ACKNOWLEDGMENTS


















Delhi 1980)



1523102242 On Sat 11 Oct 2014 135345












IV CONCLUSIONS







ACKNOWLEDGMENTS


















Delhi 1980)



1523102242 On Sat 11 Oct 2014 135345

Documents

Strategy for optimal location and rating of wind power generator with maximization of social welfare in double auction competitive power market