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Electric Power Systems Research 108 (2014) 269– 281

Contents lists available at ScienceDirect

Electric Power Systems Research

jou rn al hom e page: www.elsev ier .com/ locate /epsr

mpact of demand response program in wind integrated distributionetwork

hyati D. Mistry, Ranjit Roy ∗

epartment of Electrical Engineering, S. V. National Institute of Technology, Surat 395007, Gujarat, India

r t i c l e i n f o

rticle history:eceived 18 May 2013eceived in revised form 24 October 2013ccepted 21 November 2013vailable online 20 December 2013

a b s t r a c t

The role of distribution network operator (DNO) becomes more crucial to manage intermittent demandedpower from distribution network. This paper presents the combined effects of demand response (DR)program, wind generator, as a renewable energy source and network reconfiguration on distributionnetwork. In this work, two DR programs are incorporated namely, emergency demand response program(EDRP) and time of use program (TOU) for demand side management. The intermittent nature of wind

eywords:istributed generatorsemand response programetwork reconfigurationadial distribution systemoltage stability index

power is characterized by Rayleigh probability distribution function considering overestimation andunderestimation cost of available wind power. To find out the optimal location and size of wind generatorsparticle swarm optimization with constriction factor (PSO-CFA) algorithm is used. IEEE standard 33-nodedistribution network is considered for the study. It is found that combined effect of DR programs, networkreconfiguration and distributed generator placement makes system economical for operation.

© 2013 Elsevier B.V. All rights reserved.

. Introduction

Uncertainty in load demand which vary from time to time,ntermittent nature of renewable energy sources, rising in greenouse effects, reduction in efficiency due to increase in losses arehe key issues to handle by distribution network operator (DNOs).o handle these problems, the following are the few solutions:i) Managing energy consumption through demand side manage-

ent or by utilizing energy efficient technology. (ii) To provideroper infrastructure in order to implement demand response pro-ram (DR) and motivating consumers to participate in DR programy awarding incentive toward reduction in electricity consump-ion during peak hours. This is carried out by inspiring customerso participate in power market operation. (iii) Operating sys-em optimally by changing system topology i.e., through networkeconfiguration. (iv) By inspiring investors to install renewablenergy sources (REGs) in distribution system for the purpose ofeduction in green house effects as well as to meet the increase in

oad demand at load end.

Automation in communication and metering and evolution ofhe deregulation in power system makes possible for the customerso participate in power markets. There are numerous DR programs

∗ Corresponding author. Tel.: +91 9904402937; fax: +91 261 2227334.E-mail addresses: rr@eed.svnit.ac.in, ranjit111@yahoo.co.uk (R. Roy).

378-7796/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.epsr.2013.11.018

through which customer can participate in power markets. Refs.[1–6] focuses on different DR programs for volt/var control, loadmanagement, enhancement of efficiency, and energy resourcemanagement in distribution system. Maximization of profit forutility and social welfare, modeling of grid, managing reserveenergy through DR, service restoration through load curtailmentand voltage control through renewable energy source are reportedin [7–11].

Attributes Reference number

Demand reduction [1]Volt/var control [2,11]Energy efficiency [2,6]Profit maximization [2,6,7]Load management/energy management [3–5,9]Planning [8]Service restoration [10]

Recently due to the impacts of greenhouse gases on the globalwarming, clean energy is promoted in many countries. Wind poweris one of the cleanest, cheapest and indigenous energy sources.However, due to the intermittent and stochastic characteristic ofwind resources, wind power is not predictable. This causes dif-ficulties in estimating suitable system reserve capacity to ensurereliable system operation. Distributed generation (DG) with an

objective of minimizing real power loss and improving voltage pro-file, reliability, network upgrading, system planning, and reactivepower planning in distribution system through wind as an energysource are reported in [12–20]. The probabilistic approach for wind

2 System

pm

pas[wdafsimrlwfpdompfr

toeA(ostwtfustiuicifrviurDp[tpmm

branch exchange is performed for power loss minimization whichresults into reduction in cost of energy loss.

The electrical equivalent of a typical branch of distribution net-work is shown in Fig. 1.

The real power loss in any branch jj in the hrth hour is given by;

70 K.D. Mistry, R. Roy / Electric Power

ower generation are reported in [21–24] for active power lossinimization and improvement in system reliability.

Attributes Reference number

Loss reduction [12,17,19,21,23]Reliability improvement [13,15,22]Cost minimization [16,17,18,20]Probabilistic approach for renewable DG consideration [13,16,17,19–24,29]Distribution system planning [14,20]

A simulation based optimization framework for analysis of DRrograms with high penetration of PHEV (plug- in hybrid vehicles)nd PV (photo-voltaic) and storage systems from customer’s per-pective as well as utility company’s perspective is presented in25]. A long term planning method to maximize the benefits of net-ork reconfiguration and distributed generation (DG) allocation inistribution network is presented in [26]. The net-present valuenalysis of the planning in electricity market scenario is carried outor renewable DG (biomass, wind, solar) and diesel-engine DGs toee their viability under bilateral and competitive market scenarioss presented in [27]. A constructive heuristic algorithm for the opti-

al distributed generation allocation associated with the optimaleconfiguration in radial distribution networks to minimize energyoss is presented in [28]. A probabilistic generation approach for

ind, solar and load model followed by network reconfigurationor loss minimization is presented in [29]. A mixed integer linearrogramming problem (MILP) is developed for reconfiguration ofistribution system in presence of distributed generation is devel-ped in [30]. Intra-day distribution system configuration based onulti scenario analysis handled with decision theory concepts is

resented in [31]. The resulting configurations are then used toormulate a demand response scheme for a given time period toeduce system losses.

The mixed integer nonlinear programming (MINLP) as an objec-ive function for minimizing the system’s annual energy losses byptimally allocating different types of renewable distributed gen-ration (DG) units with probabilistic approach is formulated in [32].

reconfiguration methodology based on an Ant Colony AlgorithmACA) for minimum power loss and increment load balance factorf radial distribution networks with distributed generators is pre-ented in [33]. A bilevel programming approach is presented in [34]o determine the optimal contract price of distribution companyith dispatchable distributed generation (DG) units considering

he owner of the DG. The chance constrained programming (CCP)ramework, a new method is presented in [35] to handle thencertainties in the optimal siting and sizing of DGs. The energyummation algorithm for energy loss allocation in radial distribu-ion networks (DN) with dispersed generation (DG) is presentedn [36]. A binary particle swarm optimization (PSO) algorithm issed in [37] to determine the optimal location, size and timing of

nvestment for both distributed generation (DG) units and networkomponents considering the uncertainties of electric load, electric-ty price and wind power generations. A stochastic multi objectiveramework is proposed in [38] for distribution feeder reconfigu-ation (DFR). The objective functions including total power losses,oltage deviation and total cost is minimized using adaptive mod-fied particle swarm optimization (AMPSO). Moreover, systemncertainties including wind power generation and active andeactive load uncertainty are explicitly considered in the stochasticFR problem. A probabilistic approach has been used for optimallacement of renewable sources for minimization of energy loss in39] by using evolution programming. Cost benefit analysis of dis-

ribution network operator is presented in [40–43] with optimallacement of renewable generator considering upgrade invest-ents, loss minimization, reliability improvement, efficient voltageanagement as an objective function. A new algorithm based on

s Research 108 (2014) 269– 281

DE is proposed in [44] to determine the best sites, sizes, and optimalpayment incentives under special contracts for committed-type DGprojects to offset distribution network investment costs.

Attributes Reference number

Price minimization [25,26,27,34,37,38,40–43,44]Peak demand reduction [25,31]Distribution system planning [26,27]Loss Reduction [28,29–33,36,38,39]Probabilistic approach for

Renewable DG consideration[32,35,37,39]

It is seen from the literature that the effect of renewable sourceuncertainty on DNO’s operating cost is addressed in literatures[40–43]. The effect of distribution system reconfiguration and DGplacement on network losses is reported in [12,38]. A probabilis-tic generation approach for wind, solar and load model followedby network reconfiguration for loss minimization is presented in[29]. But, the computation of wind power cost using overestimationand underestimation cost on DNO’s operating cost is not addressedin the literature. Moreover, the effect of DR program and renew-able sources with storage system to minimize cost is reported in[25]. But the combined effect of different DR program with systemreconfiguration, with wind uncertainty on DNO’s operating cost isnot reported in literature yet.

Main contribution of this paper is to address the followingissues:

• To study the combined effect of different DR program with systemreconfiguration on DNO’s operating cost.

• To study the combined effect of different DR programs with winduncertainty on DNO’s operating cost.

• To study the combined effect of different DR programs with sys-tem reconfiguration with wind uncertainty on DNO’s operatingcost.

In the following sections, network reconfiguration is describedin Section 2, probabilistic model of wind power generator and itscost is given in Section 3, modeling of demand response is explainedin Section 4. Load modeling is given in Section 5, mathematicalformulation of problem is explained in Section 6, Section 7 brieflydescribes the load flow technique adopted, voltage stability index,voltage deviation and optimization technique employed. A casestudy is reported in Section 8, Section 9 shows the parameters selec-tion. Section 10 discuses the outcome of this work, Section 11 showsthe comparison of PSO-CFA algorithm with existing literatures andfinally, the conclusion of the paper is summarized in Section 12.

2. Network reconfiguration

The daily load profile is divided into 24 h intervals. The probabil-ity of having that load on that time at a particular node is consideredwith normal probability distribution function (pdf). The pdf func-tion for load probability is described in section 5. In this work hourly

LPjj(hr) = I2jj(hr) × r(jj) (1)

K.D. Mistry, R. Roy / Electric Power System

w

V

T

wIrit

3

3

vdd

f

wfao

W

icb

F

Fig. 1. Electrical equivalent of a typical branch.

For the same line, given in Fig. 1, sending end voltage can beritten as:

m2(hr) = Vm1(hr) − Ijj(hr) × Zjj (2)

LP(hr) =LN1∑jj=1

LPjj(hr) (3)

here LPjj(hr) is the active power loss in branch jj in the hour hr,jj(hr) is the current in branch jj in the hour hrth, rjj is the branchesistance, Vm2(hr) is the receiving end voltage in hrth hour, TLP(hr)s the total active power loss of the system in hrth hour and LN1 ishe total number of branches.

. Modeling of wind generator output and cost

.1. Wind turbine input/output and probability functions

The wind speed is modeled using Rayleigh PDF, in which thealue adopted k = 2 is from [45]. The advantages of the Weibullistribution function is reported in [45]. The Weibull probabilityensity function (PDF) is given as:

v(v) = 2c

( vc

)exp

[−( v

c

)2]

(4)

here c > 0 and k > 0 are referred to as the scale factor and shapeactor respectively, v is the wind speed in (m/s). Some researchersdopted a simplified model of wind turbine [45,46] to calculateutput power which is given as:

=

⎧⎪⎪⎪⎪⎨⎪⎪⎪⎪⎩

0; (V < vin or V > vout)

Wr; (vr ≤ V ≤ vout)

(vav − vin) Wr

(vr − vin); (vin ≤ V≥vr)

(5)

where, vin, vr and vout are cut-in, rated and cut-out wind veloc-ty. According to Eq. (5), power output from wind turbine can beomputed and the corresponding probabilities (CDF and PDF) cane calculated as:

For V < vin or V > vout, prob (W = 0)

= Fw(W) = FV (Vin) + [1 − Fv(vout)]

= 1 − exp

(−( vin

c

)k)

+ exp

[−( vout

c

)k]

(6)

or vin ≤ V ≤ vr , prob(W = W) = FW (W)

= klvin

wrc

[(1 + lw

wr

)vin

c

]k−1

× exp

⎧⎨⎩−

[(1 + lw

wr

)vin

c

]k⎫⎬⎭ (7)

s Research 108 (2014) 269– 281 271

where l =(

vrvin

)− 1 = ratio of linear range of wind speed to cut-in

wind speed(8)For vr < V ≤ vout, prob(W = Wr) = FW (W) =FV (vout) − FV (vr) = exp

[−(

vrc

)k]

+ exp[−( vout

c

)k]

3.2. Cost of wind power generation

Cost of wind power generation includes two components:(i) Direct cost: If wind turbine is owned by the DNO, this cost may

not exist. The wind power production does not require fuel, unlessthe operator wants to assign some payback cost as maintenanceand replacement cost. In this work, it is considered that DNO has topurchase power from the Distributed generator operator (DGOs).This cost is based on the special contractual agreement [45]. A linearcost function is assumed for the wind generated power as:

CWi(Wi) = diWi (9)

Where CWi is the cost function for the ith wind generator, Wiis the wind power from the ith wind generator and di is the directcost coefficient for the ith wind generator.

(ii) Penalty: The penalty cost comprises of two components i.e.,(i) underestimation cost and (ii) overestimation cost. The underes-timation situation occurs if the available wind power is more thanthe predicted. The system operator should compensate for the notusing all the available wind power. This will be linearly related tothe difference between the available wind power and the actualwind power used. The penalty cost function for underestimation ispresented as:

CuWi(hr)(Wiav(hr) − Wipr(hr))

= kpi(Wiav(hr) − Wipr(hr))

= kpi

∫ wir

wipr (hr)

(W − Wipr(hr))FW (W)dW (10)

Overestimation cost is accounted if the actual wind power is lessthan the predicted power. The operator needs to purchase powerfrom an alternate source and pay the overestimation penalty costwhich is given in Eq. (11) as:

CoWi(hr)(Wipr(hr) − Wiav(hr))

= kri(Wipr(hr) − Wiav(hr))

= kri

∫ wiav(hr)

0

(Wiav(hr) − W)FW (W)dW (11)

where kpi = penalty cost coefficient for the ith wind generator, CuWi(hr) = penalty cost function for not using available wind power fromith wind generator in the hour hr. CoWi (hr) = required reserve costfunction in the hour hr. Wiav (hr) = available wind power from theith wind generator in hrth hour. Wipr (hr) = scheduled wind powerfrom the ith wind generator in hrth hour. FW (W) = wind power PDF.kri = penalty cost coefficient for the ith wind generator for overes-timation.

4. Demand response modeling structure

Demand response is a customer driven program in which cus-tomer’s behavior or action is modeled through incentive basedprogram. The customers intended to change the timing, level of

demand or shifting their electricity demand during peak hours tooff-peak hours. In order to formulate the participation of customersin DR programs, load economic model [47,48] is to be incorporatedin DR cost function.

2 System

c

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r

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ttpc

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P

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fb(hpo

72 K.D. Mistry, R. Roy / Electric Power

Price elasticity is a measure used in economics to evaluatehange in demand to a change in its price.

= PE0

PL0× ıPL

ıPE(12)

According to Eq. (12), price elasticity of the hrth hour withespect to jth period can be written as:

(hr, j) = PE0

PL0(hr)× ıPL(hr)

ıPE(j)(13)

Some loads are stiff which cannot be shifted form one periodo another period termed as self-elasticity and it is always nega-ive. Some loads could be transferred from peak periods to off-peakeriods and having sensitivity to multi-period can be defined asross elasticity. Its value is always positive [49].

(hr, j) ≤ 0 if hr = j (14)

(hr, j) ≥0 if hr /= j (15)

The detailed process of modeling and formulation of economicoad model with EDRP and TOU program with customer participa-ion and its benefits have been discussed in [50], which is given as:

L (hr) = PL0 (hr)

⎧⎪⎪⎪⎨⎪⎪⎪⎩

1 + E(hr, hr) × PE(hr) − PE0(hr) + INC(hr)PE0(hr)

+24∑

j = 1j /= hr

E (hr, j) × PE(j) − PE0(j) + INC(j)PE0(j)

⎫⎪⎪⎪⎬⎪⎪⎪⎭

(16)

here E(hr,hr) and E(hr,j) are the self-elasticity and cross elasticityoefficients of price elasticity matrix at hrth hour of jth period. Eq.16) shows the estimated customer participation to attain highestrofit during a period of 24 h. Assuming equal value of electricityrice before and after DR program, Eq. (16) can be modified as:

L(hr) = PL0(hr)

⎧⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎩

1 +

24∑j=hr

E(hr, j) × INC(hr)

PE0(hr)

⎫⎪⎪⎪⎪⎪⎬⎪⎪⎪⎪⎪⎭

(17)

The execution of DR program leads to an extra cost for ISO oper-tor to the consumer for their participation.

(hr) = −

˛INC2(hr)PL0(hr)24∑

j=hr

E(hr, j)

(18)

DR PE0(hr)

Cost of DR is an amount paid by an ISO operator to the customeror their participation in DR program which is largely influencedy an incentive value. In emergency demand response programEDRP), incentive is offered during peak hours only and rest of theours it is considered as zero. While in time of use demand responserogram (TOU), different incentive values are offered during low,ff-peak and peak hours to reduce electricity consumption.

s Research 108 (2014) 269– 281

where, E Elasticity of thedemand

PE Electricity energy price($/kWh)

PL The demand value(kWh)

PE0 Initial electricity price($/kWh)

PL0 Initial demand value(kWh)

INC(hr) Incentive in hrth hour($/kWh)

˛ Customer participationfactor = 0.4

5. Load modeling

The load on a system is varying with respect to time. The load oneach bus is modeled as a normal PDF which is adopted from [20]:

F(SDi (hr)) = 1√

(2��Di

(hr))exp

[− (SD

i(hr) − �D

i(hr))

2

2�2

](19)

where SDi

(hr) is the apparent power demand in bus i in hrth hour,and �D

i(hr) and �D

i(hr) are the mean and variance of demand in bus

i, in hrth hour respectively. The load profile of each node is given inFig. 2.

6. Problem formulation

The proposed objective functions for different test cases aredefined as:

Case 1With base test system.

Of1(hr) = pgrid(hr) × cgrid + TLP(hr) × cgrid (20)

Where,

pgrid (hr) =NB∑i=1

PLi (hr) (21)

Case 2With system reconfiguration.

Of2(hr) = pgrid(hr) × cgrid + TLP re(hr) × cgrid (22)

Case 3With wind uncertainty. In this case wind turbine is used as a

Distributed generator. In this case part of the demanded power issupplied by wind generator when wind power is available. In thispaper it is assumed that wind power is available from 10.0 h to20.0 h.

Of3(hr) = pgrid(hr) × cgrid + TLP(hr) × cgrid +2∑

i=1

CDGWi(hr) (23)

Where,

CDGWi(hr) = CWi(Wi) +ndg∑i=1

CuWi(hr)(Wiav(hr) − Wipr(hr))

+ndg∑

CoWi(hr)(Wipr(hr) − Wiav(hr)),

i=1

for hr = 10, 11, . . ., 20 h. (24)

Case 4With demand response program. Two different DR programs has

been implemented i.e., TOU and EDRP respectively. The objective

K.D. Mistry, R. Roy / Electric Power Systems Research 108 (2014) 269– 281 273

node

f

O

O

g

O

O

pg

O

O

O

Fig. 2. Hourly load profile for 33

unction now becomes

f4 TOU(hr) = pgrid(hr) × cgrid + TLP(hr) × cgrid + CDR EDRP(hr) (25)

f4 EDRP(hr) = pgrid(hr) × cgrid + TLP(hr) × cgrid + CDR EDRP(hr) (26)

Case 5With DR program with reconfiguration. In this case first DR pro-

ram is implemented and then system is reconfigured in each hour.

f5 TOU(hr) = pgrid(hr) × cgrid + TLP re(hr) × cgrid + CDR TOU(hr)

(27)

f5 EDRP(hr) = pgrid(hr) × cgrid + TLP re(hr) × cgrid + CDR EDRP(hr)

(28)

Case 6With DR program with wind uncertainty. In this case first DR

rogram is executed and then optimal location and capacity of windenerator is found out using evolutionary technique.

f6 TOU(hr) = pgrid(hr) × cgrid + TLP(hr) × cgrid + CDR TOU(hr)

+2∑

i=1

CDGWi(hr) (29)

f6 EDRP(hr) = pgrid(hr) × cgrid + TLP(hr) × cgrid + CDR EDRP(hr)

+2∑

i=1

CDGWi(hr) (30)

Case 7With DR program with wind uncertainty with reconfiguration.

f7 TOU(hr) = pgrid(hr) × cgrid + TLP re(hr) × cgrid + CDR TOU(hr)

+2∑

i=1

CDGWi(hr) (31)

network with load probability.

Of7 EDRP(hr) = pgrid(hr) × cgrid + TLP re(hr) × cgrid + CDR EDRP(hr)

+2∑

i=1

CDGWi(hr) (32)

Subject to:Equality constraint: Power balance equation

Pi − PDGi − PLi = 0 (33)

Inequality constraints: voltage magnitude at each node must liewithin their permissible ranges to maintain power quality:

|Vmini | ≤ |V | ≤ |Vmax

i | (34)

where,hr = Hour indices 1, 2,. . ., 24 TLP re (hr) =

total active power loss afterreconfiguration in hourhr

pgrid(hr) = power demand inhourhr

CDGwi (hr) =cost of ith wind generator in hourhr

cgrid = cost of purchased powerfrom grid = 0.07 $/kWh

CDR TOU(hr) = cost of DRprogram($/kWh)

TLP(hr) = total active power lossin hourhr

CDR EDRP(hr) = cost of EDRPprogram($/kWh)

NB = number of buses PLi = active power load at busiVmin

i=

minimum voltage limit at busiVmax

i= Maximum voltage limit at busi

7. Solution techniques

7.1. Load flow solution

A simple forward-backward algorithm that is based on basiccircuit theory is used. It is assumed that the three-phase radialdistribution network is balanced and can be represented by theirequivalent single-line diagram as shown in Fig. 1. The iteration cyclewill stop if the difference in previous voltage value and new voltagevalue (0.0001) is reached.

7.2. Voltage stability index

For the calculation of voltage stability index the power flowbased formula [51] is used in this work.

2 Systems Research 108 (2014) 269– 281

a

V

it

7

qpd

V

v

7

st

7

nmseambtcauu(emanP

l

v

ivrat

1 1.5 2 2.5160

170

180

190

C1,C2

of3

74 K.D. Mistry, R. Roy / Electric Power

The voltage stability index at node m2 in the hour hr is calculateds:

SIm2(hr) = |Vm1(hr)|4 − 4{Pm2(hr)r(ij) − Qm2(hr)xij}2

− 4{Pm2(hr)r(jj) + Qm2(hr)x(jj)}|Vm1(hr)|2 (35)

Condition for stable operation of the radial distribution networks VSIm2 (hr) ≥ 0, for m2 = 2, 3. . ., NB. The node where VSIm2 is foundo be minimum is the most sensitive to voltage collapse.

.3. Deviation in the bus voltage

Bus voltage is one of the most significant security and poweruality indices. The variation in voltage magnitude result into poorerformance of electrical system. The voltage deviation can beescribed as follows:

deviation =NB∑i=1

|Vrated − Vi|Vrated

(36)

where, Vrated = nominal voltage of the system = 1.0 p.u, Vi = realoltage of the ith bus.

.4. Optimization technique employed

The optimization algorithm employed in this paper is Particlewarm optimization with constriction factor (PSO-CFA) to computehe optimum location and size of multiple numbers of DGs.

.5. Particle swarm optimization with constriction factor

Hybrid particle swarm optimization [52,53] uses the mecha-ism of particle swarm optimization (PSO) and a natural selectionechanism, which is usually utilized by evolutionary computation

uch as genetic algorithms (GA). For instance, the number of highlyvaluated agent is increased while the number of lowly evaluatedgents is decreased in each iteration. Since search procedure by PSOainly depends on pbesti and gbest, the search criteria is limited

y pbesti and gbest. Namely, using pbesti and gbest, PSO changeshe current search points independently. On the contrary, PSOCFAan jump the current search points into the effective (attractive)rea directly by the selection mechanism. Those with high eval-ation values, replace agent positions with low evaluation valuessing the selection. The replacement rate is called selection ratesr). Unlike other evolutionary computational methods, CFA of PSOnsures the convergence of the search procedures based on theathematical theory. CFA considers dynamic behavior of one agent

nd the effect of the interaction among agents. Selection mecha-ism is also adopted in this technique like GA, so the name hybridSOCFA (PSOCFA) is maintained.

The velocities of the particles are modified according to the fol-owing equation:

k+1i

= CFA∗ × [vki + c1 × rand1 × (pbesti − sk

i ) + c2 × rand∗2

× (gbest − ski )] (37)

where CFA = 2/|2� −√

�2 − 4�|. � = c1 + c2, � > 4. For example,f � = 4.1, then CFA = 0.73. As � increases above 4.0, CFA gets smaller.

ki

is the velocity of agent i at iteration k, rand1 and rand2 are theandom numbers between 0 and 1. sk

iis the current position of

gent i at iteration k, pbesti is the personal best of agent i, gbest ishe group best of the group.

Fig. 3. Variation of c1, c2 for Of3.

A low value of CFA facilitates rapid convergence and little explo-ration whereas high values of CFA give slow convergence and muchexploration. CFA = 0.73 is chosen as the best value.

Position updating equation:

sk+1i

= ski + vk+1

i(38)

During the simulation of PSO, the best chosen maximum popula-tion size = 50, maximum allowed iteration cycles = 100. The choiceof c1, c2 are very much vulnerable for PSO execution. The best valueof either c1 or c2 is 2.05 found out after several experimentation.

Implementation of PSO-CFA algorithm for multiple wind gener-ator placement:

Step 1: Initial population PDGi = [PDG1, PDG2, PDG3,. . .,PDGn] of ran-domly distributed solution is generated from the multidimensional search space, where n is the size of optimizedparameters. In this work, n is considered as the number ofDG to be placed and PDG1, PDG2,. . ., PDGn are possible can-didate solution depending upon the number of DG to beplaced. The capacity of DG ranges between maximum andminimum specified limits is described as:

PDGi = PDG min + rand ∗ (PDG max − PDG min) (39)

where, PDGmin and PDGmax are the minimum and maximumcapacity of DG, rand = random number between 0 and 1.

In this case, capacity of DG is assumed as particles andthe location of DG is randomly placed at the node at whichload is connected.

Step 2: Evaluate each particle’s position according to objectivefunction Eq. (23) and Eqs. (29–32).

Step 3: If a particle’s current position is better than its previous bestposition then update it.

Step 4: Determine the best particle.Step 5: Update particle velocity according to Eq. (37).Step 6: Compute the new positions of the particle according to Eq.

(38).Step 7: Go to step 2 until stop criteria is satisfied.

7.6. Parameter selection of PSO-CFA algorithm

The parameters of PSO-CFA algorithm are chosen after severalruns. Figs. 3 and 4 show the variation of PSO-CFA parameters at time20th hour. c1 and c2 are varied keeping number of population = 50and maxcycle = 100 as fixed and value of of3 is computed. The valuesof c1 and c2 are varied from 1.0 to 2.5. It is observed from Fig. 3 thatwith c1 = c2 = 2.05 yields least cost. Moreover, number of populationnp is varied from 10 to 100 keeping c1 = c2 = 2.05 and maxcycle = 100

as fixed and the value of of3 is computed. It is found from Fig. 4 thatnp = 50 yields minimum cost. Hence, c1 = c2 = 2.05 and np = 50 givesthe optimum results which is taken as input parameters in PSO-CFAalgorithm.

K.D. Mistry, R. Roy / Electric Power Systems Research 108 (2014) 269– 281 275

Table 1Parameters for wind generators.

Parameters Rated power (MW) Vin (m/s) Vr (m/s) Vout (m/s) c d kp kr

Turbine 1 1.1 4 14

Turbine 2 1.1 4 14

10 20 30 40 50 60 70 80 90 10 0160

170

180

190

200

210

220

of3

8

s

Mp

9

9

T

9

o(acits

Number of population

Fig. 4. Variation of np for Of3.

. System under study

For this work, 33-node radial distribution network [54] iselected for the study.

For 33-node network [54], substation voltage = 12.66 kV, baseVA = 100, total active power load = 3.715 MW and total reactive

ower load = 2.3 MVAr. The voltage limit is 0.90 p.u to 1.05 p.u.

. Parameter selection

.1. Wind generator parameters

The parameters for two wind generators are presented inable 1.

.2. DR program parameters

The entire load demand curve is divided into various periodsf a day, namely low period (00.00–05.00 h), off-peak period05.00–09.00 h and 14.00–19.00 h) and peak period (09.00–14.00 hnd 19.00-24.00 h), respectively. This study has considered 40%

ustomer participation in DR program [49]. EDRP offers highncentive value during peak hours in order to encourage customero reduce their load demand and get financial payback. Thistudy assumes $10 as a peak hour incentive. TOU offers different

Fig. 5. Hourly values of the objectiv

24 10 2 5 724 15 2 5 7

incentive values for different periods of a day. This study has con-sidered $4, $7 and $10 as low, off-peak and peak hour incentives,respectively. Spot price for different hours is given in [55]. Thevalue of price elasticity matrix used is given in [49].

10. Discussion on the results

Fig. 5 shows the values of different objective function for 24 hduration for all nodes. Fig. 6 represents the cost of purchased energyfrom the Transco for 24 h duration. Fig. 7 shows the cost of energyloss for 24 h duration. Figs. 8 and 9 represent the active power lossand reactive power loss for 24 hour duration.

Case 1This case is the base case. The load curve of the same is presented

in Fig. 2.Case 2In this case system is reconfigured at each hour as the load pat-

tern changes. The hourly switching action is shown in Table 2. Fromthe Table 3 it is seen that the % saving in cost of energy loss is 31%while saving in purchase energy from Transco is 4.3%. It is also seenfrom Table 3 that the % reduction in active and reactive power lossis 31% and 23.7% compared to case 1.

Case 3In this case, two wind generators of capacity 1.1 MW are

considered. Particle swarm optimization with constriction factor(PSO-CFA) optimization algorithm is adopted to find out optimumsize and optimum location of two DGs. As the cost of wind poweris very less compared to other conventional source, all the avail-able wind power is injected in to the system. The penalty due tounderestimation cost is zero. The only penalty DNO has to pay isdue to overestimation cost of wind power. The optimal location fortwo wind generator is found to be at node 29 and 9, respectively.The penalty for overestimation is 4.1292 and 2.9805 $/hr for boththe generator respectively. Total cost of wind power generator is11.5298 $/hr for 2.2 MW power output. From the Table 3 it is seen

that the % saving in cost of energy loss is 33.2% while in saving inpurchase energy from Transco is 26.8%. It is also seen from Table 3that the % reduction in active and reactive power loss is 33.2% and32.8% compared with Case 1.

e function for different cases.

276 K.D. Mistry, R. Roy / Electric Power Systems Research 108 (2014) 269– 281

Fig. 6. Hourly cost of purchased energy from the Transco for different cases.

ergy

di

Fig. 7. Hourly cost of en

Case 4In this case, two demand response programs, namely emergency

emand response program (EDRP) and time of use (TOU) have beenmplemented. Cost of DR is zero other than peak period for EDRP

Fig. 8. Hourly active power l

loss for different cases.

based DR program. From the Table 3 it is seen that the % saving incost of energy loss is 4.5%, saving in purchase energy from Transcois 2.8% and saving in total cost is 2.7% in EDRP while in TOU programit is 2.7%, 1.7% and 1.6%, respectively. It is also seen from Table 3 that

oss for different cases.

K.D. Mistry, R. Roy / Electric Power Systems Research 108 (2014) 269– 281 277

ower

tEciis

rIttpe

TH

Fig. 9. Hourly reactive p

he % reduction in active and reactive power loss is 4.5% and 4.5% inDRP while in TOU it is 2.7% and 2.7% respectively compared withase 1. It is clear from Table 3 that cost of DR for TOU based programs less compared with EDRP based program. The load reduction aftermplementing TOU based DR and EDRP based DR at node 3 arehown in Figs. 10 and 11.

Case 5In this case first DR program is executed and then system is

econfigured in each hour for minimization of active power loss.t is clear from Table 3 that reduction in active power loss, dras-

ically reduces the cost of energy loss. From the Table 3 it is seenhat the % saving in cost of energy loss is 33.8%, saving in cost ofurchase energy from Transco is 2.8% and saving in total cost ofnergy is 6.9% in EDRP while in TOU program it is 32.8%, 1.7% and

able 2ourly switching patterns for different cases.

Open branches

Case-2 Case-5

TOU EDRP

Hours Hours Switching pattern Hours Switching pattern Hours Sw

1 1 7,9,14,32,37 1 7,9,14,32,37 1 7,92 2–3 7,9,14,28,32 2–3 7,9,14,28,32 2–3 7,934 4 7,9,14,32,37 4 7,9,14,32,37 4 7,95 5–8 7,9,14,28,32 5 7,9,14,28,32 5–8 7,96 6–7 7,9,14,32,37

78 8 7,9,14,28,32

9 9–10 7,9,14,32,37 9–10 7,9,14,32,37 9–10 7,91011 11–12 7,9,14,28,32 11–12 7,9,14,28,32 11–12 7,91213 13–23 7,9,14,32,37 13–22 7,9,14,32,37 13–22 7,914151617181920212223 23–24 7,9,14,28,32 23–24 7,924 24 7,9,14,28,32

loss for different cases.

5.8%, respectively. It is also seen from Table 3 that the % reductionin active and reactive power loss is 33.8% and 26.7% in EDRP whilein TOU it is 32.8% and 25.8% respectively compared with case 1.Table 2 shows the switching pattern for TOU and EDRP case.

Case 6In this case combine effect of DR program and DG placement has

been studied. To see the effect, first DR program is executed andthen optimal location and capacity of wind generator is found out.The optimal location and size of two wind generators are found outwith the help of PSO-CFA algorithm. The optimal location for two

wind generator is at node 29 and 9 respectively. From the Table 3it is seen that the % saving in cost of energy loss is 35.5%, saving inpurchase energy from Transco is 30.67% and saving in total cost ofenergy is 29.3% in EDRP while in TOU program it is 34.2%, 29.5% and

Case-7

TOU EDRP

itching pattern Hours Switching pattern Hours Switching pattern

,14,32,37 1 7,9,14,32,37 1 7,9,14,32,37,14,28,32 2–3 7,9,14,28,32 2–3 7,9,14,28,32

,14,32,37 4 7,9,14,32,37 4 7,9,14,32,37,14,28,32 5 7,9,14,28,32 5–8 7,9,14,28,32

6–7 7,9,14,32,37

8 7,9,14,28,32,14,32,37 9–10 7,9,14,32,37 9–10 7,9,14,32,37

,14,28,32 11–12 7,9,14,28,32 11–12 7,9,14,28,32

,14,32,37 13–22 7,9,14,32,37 13–22 7,9,14,32,37

,14,28,32 23–24 7,9,14,28,32 23–24 7,9,14,28,32

278 K.D. Mistry, R. Roy / Electric Power Systems Research 108 (2014) 269– 281

Table 3Comparative study of different cases.

Parameters Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Case 7

TOU EDRP TOU EDRP TOU EDRP TOU EDRP

Cost of purchased energyfrom Transco.($/day)

6085.8 6085.8 4391.8 5979.6 5913.1 5979.6 5913.1 4285.6 4219.1 4285.6 4219.1

% saving in cost ofpurchased energy

– 0 27.8 1.7 2.8 1.7 2.8 29.5 30.6 29.5 30.6

Cost of energy loss($/day)

1001.0 690.3 668.7 973.8 955.9 672.3 662.6 658.0 644.8 497.7 492.9

% saving in cost of energyloss

– 31 33.2 2.7 4.5 32.8 33.8 34.2 35.5 50.2 50.7

Cost of windpower($/day)

– – 126.8 – – – – 126.9 126.9 126.9 126.9

Cost of DR ($/day) – – – 18.6 24.5 18.6 24.5 18.6 24.5 18.6 24.5Total cost of energy

($/day)7087.0 6776.3 5187.4 6972.1 6893.7 6670.6 6595.4 5089.2 5015.4 4928.9 4863.4

% saving in total cost ofenergy

– 4.3 26.8 1.6 2.7 5.8 6.9 28.1 29.3 30.4 31.3

Active power loss (kW) 4767.7 3286.6 3184.8 4637.9 4552.7 3201.2 3154.4 3134.1 3071 2370.4 2347.0% Reduction in active

power loss– 31 33.2 2.7 4.5 32.8 33.8 34.2 35.5 50.2 50.7

Reactive power loss(KVAr)

3178.6 2424.3 2134.4 3092.4 3035.5 2357.7 2326.8 2101 2059.3 1702.8 1691.0

% Reduction in reactivepower loss

– 23.7 32.8 2.7 4.5 25.8 26.7 33.9 35.2 46.4 46.8

Minimum voltage 0.8985 0.9306 0.9582 0.9024 0.9024 0.9334 0.9334 0.9581 0.9581 0.9809 0.9809Minimum VSI 0.6516 0.7498 0.8430 0.6631 0.6641 0.7591 0.7591 0.8427 0.8428 0.9324 0.9324

5 10 15 2080

100

120

140

160

Rea

l p

ow

er l

oa

d (

kW

)

Aftre DR-TOU

Syst em only

2ii

t

Hours

Fig. 10. Effect of TOU program on load curve at node 3.

8.1% respectively. It is also seen from Table 3 that the % reductionn active and reactive power loss is 35.5% and 35.2% in EDRP whilen TOU it is 34.2% and 33.9%, respectively compared with case 1.

Case 7In this case combine effect of DR program, network reconfigura-

ion and DG placement has been studied. To see the effect, first DR

2 4 6 8 10 12 14 16 18 20 22 2480

90

100

110

120

130

140

150

160

Hours

Rea

l p

ow

er l

oa

d(k

W)

After DR-EDRP

System only

Fig. 11. Effect of EDRP program on load curve at node 3.

program is executed and then system is reconfigured in each hourand then DG is placed optimally. The optimal location and size oftwo wind generators are found out with the help of PSO-CFA algo-rithm. The optimal location for two wind generator is found out tobe at node 29 and 9 respectively. From the Table 3 it is seen thatthe % saving in cost of energy loss is 50.76%, saving in purchasecost of energy from Transco is 30.67% and saving in total cost ofenergy is 31.3% in EDRP while in TOU program it is 50.27%, 29.5%and 30.4%, respectively. It is also seen from Table 3 that the % reduc-tion in active and reactive power loss is 50.7% and 46.8% in EDRPwhile in TOU it is 50.2% and 46.8% respectively which is a signifi-cant reduction compared with all cases. It is also seen from Table 3that the reduction in purchase power from Transco, cost of energyloss, and total cost of energy, active power loss and reactive powerloss are maximum with EDRP program compared to all other cases.Fig. 12 shows the convergence characteristic of PSO-CFA algorithmfor objective function 7 at hour 20. Moreover, minimum voltagemagnitude and minimum VSI obtained in case 7 is highest which

proves the combined effect of DR program, network reconfigura-tion and DG placement in distribution system.

0 20 40 60 80 100135

140

145

150

Iter ation cycles

Of7

__

ED

RP

at

ho

ur

20

Fig. 12. Convergence characteristic of PSO-CFA for hour 20.

K.D. Mistry, R. Roy / Electric Power Systems Research 108 (2014) 269– 281 279

Table 4Comparison of active power losses as objective function for base case with reported optimization algorithm.

Optimizationtechniques

Switches opened Optimum place(node no)

Optimum DG size(MW)

Total DG size Power loss (MW)

Without DG 33,34,35,36,37 0.2026Only DG 1 DG ABC [56] 33,34,35,36,37 6 2.5775 2.5775 0.1050

PSO [57] 6 2.5775 2.5775 0.1052FUZZY [58] 32 1.2931 1.2931 0.1270PSOCFA 6 2.5752 2.5752 0.1039

2 DG ABC [56] 33,34,35,36,37 6 1.9707 2.5464 0.089915 0.5757

PSO [57] 6 1.9707 2.5464 0.089915 0.5757

FUZZY [58] 32 0.3836 1.5342 0.117330 1.1506

PSO-CFA 29 1.2487 2.0363 0.086214 0.7876

3 DG ABC [56] 33,34,35,36,37 6 1.7569 3.1152 0.079215 0.575725 0.7826

PSO [57] 6 1.7569 3.1152 0.079215 0.575725 0.7826

FUZZY [58] 32 0.2701 1.5342 0.117330 1.113831 0.1503

HSA [12] 18 0.1070 1.7256 0.096717 0.572433 1.0462

PSO-CFA 29 0.9956 3.0080 0.07269 0.948323 1.0641

DG installation afterreconfiguration

HSA [12] 7,14,9,32,37 32 0.2686 1.0911 0.097131 0.161130 0.6612

PSO [59] 7,10,28,14,32 6 1.6725 3.3338 0.092312 0.379825 0.625532 0.6560

GA [59] 7,9,14,28,32 6 1.7454 3.8737 0.112412 0.480825 0.789032 0.8585

PSO-CFA 7,14,9,32,37 29 0.9987 2.2104 0.04609

23

1l

to

1

Fa

1. Comparison of PSO-CFA algorithm with existingiterature

To show the optimality of the PSO-CFA method, two cases areaken from the literature in which active power loss is taken as thebjective function. The cases are:

. With DG: In this study, single DG, two DG and three DG cases aretaken for comparison.

0 20 40 60 80 10 00.08

0.09

0.1

0.11

0.12

Iteration cycles

Act

ive

po

wer

lo

ss (

MW

)

ig. 13. Convergence characteristic of PSO-CFA for 2 DG case for active power losss an objective function.

0.1822 1.0295

2. DG installation after reconfiguration.

From the Table 4, it is clear for the first case that for single DG,two DG and three DG case, PSO-CFA yields minimum loss comparedwith ABC [56], PSO [57], HSA [12] and Fuzzy [58] based algorithm.In second case, it is also found that PSO-CFA yields minimum losscompared with HSA [12], PSO [59] and GA [59] algorithm. Fig. 13shows the convergence characteristic of PSO-CFA for two DG casewhere active power loss is taken as an objective function.

12. Conclusion

In this work, combined effect of DR program, intermittent natureof wind power generator and network reconfiguration has beenstudied. Emergency demand response program and time of use pro-gram are implemented to study the impact of DR in distributionnetwork. IEEE 33-node standard test case is taken under study. Theintermittent nature of wind is represented by Relaying pdf functionand also penalty made toward the underestimation and overes-timation is also included in the study. The optimal location and

optimal size of distributed generator is computed with the help ofparticle swarm optimization with constriction factor approach. Theimpact of DR program, network reconfiguration and DG placementtogether in distribution system is established.

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80 K.D. Mistry, R. Roy / Electric Power

eferences

[1] S. Gyamfi, S. krumdieck, Scenario analysis of residential demand response atnetwork peak periods, Electric Power System Research 93 (2012) 32–38.

[2] N. Venkatesan, J. Solanli, S. Solanki, Residential demand response model andimpact on voltage profile and losses of an electric distribution network, AppliedEnergy 96 (2012) 84–92.

[3] M. Juan, L. Rojas, C. Monteiro, R. Dufo-Lopez, J.L. Bernal-Agustin, Optimum resi-dential load management strategy for real time pricing (RTP) demand responseprograms, Energy Policy 45 (2012) 671–679.

[4] H.S.V.S. Kumar nunna, S. Doolla, Demand response in smart distribution systemwith multiple microgrids, IEEE Transaction on Smart Grid 3 (2012) 1641–1649.

[5] Z. Vale, H. Morais, P. Faria, C. Ramos, Distribution system operation sup-ported by contextual energy resource management based on intelligent SCADA,Renewable Energy 52 (2013) 143–153.

[6] R. Dashti, S. Afsharnia, Demand response regulation modeling based on dis-tribution system asset efficiency, Electric Power System Research 81 (2011)667–676.

[7] J.M. Yusta, H.M. khodr, A.J. Urdaneta, Optimal pricing of default customersin electrical distribution systems: effect behavior performance of demandresponse models, Electric Power System Research 77 (2007) 548–558.

[8] J. Medina, I. Roytelman, Demand response and distribution grid opera-tions: opportunities and challenges, IEEE Transaction on Smart Grid 1 (2010)193–198.

[9] P. Faria, T. Soares, Energy and reserve provision dispatch considering dis-tributed generation and demand response, in: 3rd IEEE PES Innovative SmartGrid Technologies Europe (ISGT Europe), Berlin, 2012, 2012.

10] M.R. Kleinberg, H. Ching, Improving service restoration of power distributionsystems through load curtailment of in service customers, IEEE Transaction onPower Systems 26 (2011) 1110–1117.

11] K. Kawamura, R. Kato, S. bando, M. Takahashi, Avoided cost and effects of volt-age control by demand response in a distribution network with high integrationof rooftop PV, in: IEEE PES Innovative Smart Grid Technologies (ISGT), 2013,2013.

12] R.S. Rao, K. Ravindra, K. Satish, S.V.L. Narsimham, Power loss minimi-zation in distribution system using network reconfiguration in the presenceof distributed generation, IEEE Transaction on Power System 28 (2013)317–325.

13] Z. Qin, W. Li, Generation system reliability evaluation incorporating correla-tions of wind power speeds with different distributions, IEEE Transaction onPower Systems 28 (2013) 551–558.

14] E. naderi, H. Seifi, M.S. Sepasian, A dynamic approach for distribution systemplanning considering distributed generation, IEEE Transaction on Power Deliv-ery 37 (2012) 1313–1322.

15] Y. Xu, C. Singh, Adequacy and economy analysis of distribution system inte-grated with electric energy storage and renewable energy resources, IEEETransaction on Power Systems 27 (2012) 2332–2341.

16] M. Dadkhan, B. Venkatesh, Cumulant based stochastic reactive power planningmethod for distribution systems with wind generators, IEEE Transaction onPower Systems 27 (2012) 2351–2359.

17] T. Niknam, M. Zare, J. Aghaei, Scenario-based multiobjective volt/var control indistribution networks including renewable energy sources, IEEE Transactionon Power Delivery 27 (2012) 2004–2018.

18] A. Gabash, P. Li, Active–reactive optimal power flow in distribution networkswith embedded generation and battery storage, IEEE Transaction on PowerSystems 27 (2012) 2026–2035.

19] A. soroudi, Possibilistic-scenatio model for DG impact assessment on distribu-tion networks in an uncertain environment, IEEE Transaction on Power Systems27 (2012) 1283–1293.

20] K. Zou, A.P. agalgaonkar, K.M. Muttaqui, S. Perera, Distribution system plan-ning with incorporating DG reactive capability and system uncertainities, IEEETransaction on Sustainable Energy 3 (2012) 112–123.

21] A. Soroudi, M. Aien, M. Ehsan, A probabilistic modeling of photo voltaic mod-ules and wind power generation impact on distribution network, IEEE SystemsJournal 6 (2012) 254–259.

22] Y.M. Atwa, M.M.A. Salama, R. Seethpathy, M. Assam, S. Conti, Adequacy eval-uation of distribution system including wind/solar DG during different modesof operation, IEEE Transaction on Power Systems 26 (2011) 1945–1952.

23] Y.M. Atwa, E.F. EI-Saadany, Probabilistic approach for optimal allocation ofwind-based distributed generation in distribution systems, IET RenewablePower Generation 5 (2011) 79–88.

24] G. Mokryani, P. Siano, Evaluating the integration of wind power into distribu-tion networks by using Monte Carlo simulation, Electrical Power and EnergySystems 53 (2013) 244–255.

25] J. Zhao, S. Kucuksari, E. Mazhari, Y.J. Son, Integrated analysis of high-penetrationPV and PHEV with energy storage and demand response, Applied Energy 112(2013) 35–51.

26] A. zidan, M.F. Shaaban, E.F. EI-Saadany, Long-term multi objective distributionnetwork planning by DG allocation and feeders’ reconfiguration, Electric PowerSystems Research 105 (2013) 95–104.

27] N. Jain, S.N. Singh, S.C. Srivastava, A generalized approach for DG planning

and viability analysis under market scenario, IEEE Transaction on IndustrialElectronics 60 (2013) 5075–5085.

28] G.J.S. Rosseti, E.J. De Oliveira, L.W. De Oliveira, I.C. Silva Jr., Optimal allocationof distributed generation with reconfiguration in electric distribution systems,Electric Power System Research 103 (2013) 178–183.

[

s Research 108 (2014) 269– 281

29] A. Zidan, E.F. EI-Saadany, Distribution system reconfiguration for energy lossreduction considering the variability of load and local renewable generation,Energy 59 (2013) 698–707.

30] J.F. Franco, M.J. Rider, M. Lavorato, R. Romero, A mixed-integer LP model forthe reconfiguration of radial distribution system considering distributed gen-eration, Electric Power System Research 97 (2013) 51–60.

31] L. Coroama, G. Chicco, M. Gavrilas, A. Russo, Distribution system optimizationwith intra-day network reconfiguration and demand reduction procurement,Electric Power System Research 98 (2013) 29–38.

32] Y.M. Atwa, E.F. El-Saadany, M.M.A. Salama, R. Seethapathy, Optimal renewableresources mix for distribution system energy loss minimization, IEEE Transac-tions on Power Systems 25 (2010) 360–370.

33] Y. Wu, C. Lee, L. Liu, S. Tsai, Study of reconfiguration for the distribution systemwith distributed generators, IEEE Transactions on Power Delivery 25 (2010)1678–1685.

34] J. López-Lezama, A. Padilha-Feltrin, J. Contreras, J. Ignacio Munoz, Optimalcontract pricing of distributed generation in distribution networks, IEEE Trans-actions on Power Systems 26 (2011) 128–136.

35] Z. Liu, F. Wen, G. Ledwich, Optimal siting and sizing of distributed generatorsin distribution systems considering uncertainties, IEEE Transactions on PowerDelivery 26 (2011) 2541–2551.

36] M. Atanasovski, R. Taleski, Energy summation method for loss allocation inradial distribution networks with DG, IEEE Transactions on Power Systems 27(2012) 1433–1440.

37] A. Soroudi, M. Afrasiab, Binary PSO-based dynamic multi-objective model fordistributed generation planning under uncertainty, IET Renewable Power Gen-eration 6 (2012) 67–78.

38] T. Niknam, A. Kavousifard, J. Aghaei, Scenario-based multiobjective distribu-tion feeder reconfiguration considering wind power using adaptive modifiedparticle swarm optimisation, IET Renewable Power Generation 6 (2012)236–247.

39] D.K. Khatod, V. Pant, J. Sharma, Evolutionary programming based optimal place-ment of renewable distributed generators, IEEE Transactions on Power Systems28 (2013) 683–695.

40] M.F. Shaaban, Y.M. Atwa, E.F. El-Saadany, DG allocation for benefit maximi-zation in distribution networks, IEEE Transactions on Power Systems 28 (2013)639–649.

41] Z. Hu, F. Li, Cost-benefit analyses of active distribution network management,Part II: investment reduction analysis, IEEE Transactions on Smart Grid 3 (2012)1075–1081.

42] Z. Hu, F. Li, Cost-benefit analyses of active distribution network manage-ment, Part I: annual benefit analysis, IEEE Transactions on Smart Grid 3 (2012)1067–1074.

43] H. Sugihara, K. Yokoyama, O. Saeki, K. Tsuji, T. Funaki, Economic and effi-cient voltage management using customer-owned energy storage systems ina distribution network with high penetration of photovoltaic systems, IEEETransactions on Power Systems 28 (2013) 102–111.

44] H.A. Hejazi, A.R. Araghi, B. Vahidi, S.H. Hosseinian, M. Abedi, H. Mohsenian-Rad, Independent distributed generation planning to profit both utility and DGinvestors, IEEE Transactions on power Systems 28 (2013) 1170–1178.

45] J. Hetzer, C. David, An economic dispatch model incorporating wind power,IEEE Transaction on Energy Conversion 23 (2008) 603–611.

46] X. Liu, W. Xu, Minimum emission dispatch constrained by stochastic windpower availability and cost, IEEE Transaction on Power Systems 25 (2010)1705–1713.

47] H.A. Aalami, M. Parsa Moghaddam, G.R. Yousefi, Demand response model-ing considering interruptible/curtailable loads and capacity market programs,Applied Energy 87 (2010) 243–250.

48] H.A. Aalami, M. Parsa Moghaddam, G.R. Yousefi, Modelling and prioriti-zing demand response programs in power markets, Electrical Power SystemResearch 80 (2010) 426–435.

49] A. Abdollahi, M. Moghaddam, M. Rashidinejad, M.K. Sheikh-El-Eslami, Inves-tigation of economic and environmental – driven demand response measuresincorporating UC, IEEE Transactions on Smart Grid 3 (2012) 12–25.

50] H. Aalami, G.R. Yousefi, M. Parsa Moghadam, Demand response model consid-ering EDRP and TOU programs, in: IEEE PES Transmission and DistributionConference & Exhibition, Chicago, USA, 2008, 2008.

51] S. Sivanagaraju, N. Visali, V. Sankar, T. Ramana, Enhancing voltage stability ofradial distribution systems by network reconfiguration, Electric Power Com-ponents and Systems 33 (2005) 539–550.

52] R. Roy, S.P. Ghoshal, Evolutionary computation based comparative study ofTCPS and CES control applied to automatic generation control, in: Power SystemTechnology and IEEE Power India Conference, POWERCON, New Delhi, 2008,2008.

53] R. Roy, S.P. Ghoshal, A novel crazy swarm optimized economic load dispatch forvarious types of cost functions, Electrical Power and Energy Systems 30 (2008)242–253.

54] J.Z. Zhu, Optimal reconfiguration of electrical distribution network using therefined genetic algorithm, Electric Power Systems Research 62 (2002) 37–42.

55] K. Chandram, N. Subrahmanyam, M. Sydulu, New approach with Muller methodfor profit based unit commitment, in: Power and Energy Society General

Meeting-Conversion and Delivery of Electrical Energy in 21st Century, 2008,pp. 1–8.

56] M. Padma Lalitha, N. Sinarami Reddy, V.C. Veera Reddy, Optimal DG placementfor maximum loss reduction in radial distribution system using ABC algorithm,International Journal of Reviews in Computing 3 (2010) 44–52.

System

[

[

K.D. Mistry, R. Roy / Electric Power

57] M. Padma Lalitha, N. Sinarami Reddy, V.C. Veera Reddy, Optimal DG placementfor minimum real power loss in radial distribution system using PSO, Journalof Theoretical and Applied Information Technology 13 (2005) 107–116.

58] M. Padma Lalitha, V.C. Veera Reddy, N. Sivarami Reddy, V. Usha Reddy, DGsource allocation by fuzzy and clonal selection algorithm for minimum loss in

[

s Research 108 (2014) 269– 281 281

distribution system, Distributed Generation and Alternative Energy 26 (2011)17–35.

59] W.M. Dahalam, H. Mokhlis, Network reconfiguration for loss reduction withdistributed generations using PSO, in: IEEE International conference on Powerand Energy (PECon), Malaysia, 2–5 December, 2012, 2012.