multiobjective electric distribution system expansion planning using hybrid energy hub concept

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Electric Power Systems Research 79 (2009) 899–911

Contents lists available at ScienceDirect

Electric Power Systems Research

journa l homepage: www.e lsev ier .com/ locate /epsr

ultiobjective electric distribution system expansion planning using hybridnergy hub concept

ehrdad Setayesh Nazar a,∗, Mahmood R. Haghifamb

Power and Water University of Technology, Tehran, IranTarbiat Modares University, Tehran, Iran

r t i c l e i n f o

rticle history:eceived 2 July 2007eceived in revised form 3 December 2008

a b s t r a c t

This paper presents a novel approach for optimal electric distribution system expansion planning (OED-SEP) using a hybrid energy hub concept. The proposed method uses an energy hub model to explore theimpacts of energy carrier systems on OEDSEP procedure. This algorithm decomposes the OEDSEP problem

ccepted 10 December 2008

eywords:istribution system expansion planningenetic algorithm (GA)ptimizationrimary circuits

into three subproblems to achieve an optimal expansion planning of a system in which the investmentand operational costs are minimized, while the reliability of the system is maximized. The algorithm wassuccessfully tested in the present research for an urban distribution system.

© 2008 Elsevier B.V. All rights reserved.

nergy hub

. Introduction

While theory and practice of optimal electric distribution sys-em expansion planning (OEDSEP) have advanced over the years,ome other resources can also be included in distribution networklanning exercises. An electric distribution system (EDS) may inter-hange energy with other multiple energy carrier systems (MECSs)y its nodes, known as energy hubs [1,2]. When the volume of thenergy interchanged between an EDS and other MECS is compa-able with the volume of electricity delivered to the end users byhe EDS, the OEDSEP results may considerably be different from theondition that no energy is interchanged.

Over the years, OEDSEP has received much attention in the liter-ture. Ref. [3] uses a branch and bound model for choosing optimalubstation locations, while [4] employs a genetic algorithm. In [5],n evolutionary algorithm is used for planning of a medium volt-ge distribution network. In [6], an integral planning procedure haseen used for primary and secondary distribution systems using aixed integer linear programming. Ref. [7] proposes a possibilistic
odel based on the fuzzy sets for the optimal planning of distribu-
ion networks. In [8], a dynamic programming algorithm has beensed while in [9], a distribution network planning procedure haseen proposed based on statistical load modeling applying genetic

∗ Corresponding author at: Power and Water University of Technology, P.O. Box6765-1719, Tehran, Iran. Tel.: +98 2173932528; fax: +98 2177310425.

E-mail address: [email protected] (M.S. Nazar).

378-7796/$ – see front matter © 2008 Elsevier B.V. All rights reserved.oi:10.1016/j.epsr.2008.12.002

algorithms and Monte Carlo simulations. Refs. [10–12] have foundthe optimum solutions from a list of candidates, while [13,14] havefound the solution automatically.

The energy hub concept is an emerging technology in powersystems. Accordingly, an energy hub impacts on OEDSEP proce-dure, changes the EDS costs and reliability indices. It seems thatthis matter still demands more attention.

In the present research, a new OEDSEP algorithm is proposedusing the energy hub concept. First, a generalized energy hub modelis proposed. Then, a three-stage optimization procedure is pre-sented that minimizes total system costs at the first stage anddetermines the optimal allocations, capacity selection and replace-ment alternatives of the system devices at the second stage. Finally,at the third stage, the optimal restoration (OR) problem investigatesthe adequacy of system resources in contingent conditions.

The contribution of this research can be summarized as follows:

(1) It represents a new OEDSEP formulation, which finds the opti-mum usage of energy hubs for minimizing the utility costs andmaximizing the system’s reliability. It considers the impact ofenergy hub on the distribution system planning and operationscenarios.

(2) It considers the contribution scenarios of energy hubs in the
restoration procedure of the distribution system.
The remaining sections of this research are organized as follows:A brief model of the problem is presented in Section 2. In Sec-

tion 3, details of the proposed formulation are introduced. The

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dx.doi.org/10.1016/j.epsr.2008.12.002

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olution algorithm is presented in Section 4. The obtained numeri-al results are presented and discussed in Section 5. A brief reviewf the research is included in Section 6.

. Problem description

The comprehensive distribution resource planning criteria cane presented to:

1. Minimizing the utility cost of the provided service, including thecapital costs of system resources, as well as the operating costsof the existing and new facilities.

. Maximizing the value of the provided services to the consumers.

. Providing an adequate reliability of the services as well as fastand suitable system restoration.

The first alternative criterion tries to minimize the value of all theosts including the utility costs. It is also known as cost minimiza-ion criterion [15]. The second criterion tries to maximize the socialelfare, measured as the sum of consumer plus producer and utility

urplus. These two criteria are equivalent, assuming that the priceshat the consumers must pay are very weakly dependent upon thexpansion plan of the system, or the main component of the cus-omers’ surplus, affected by different system plans, is the customernterruption cost (CIC), which must be minimized. These objectivesre conflicting, because high reliability levels may require morenvestment in the expansion and maintenance of system resources.he provided reliability of service is an integral component of therst and second planning criteria, and must be maximized. Thus,he OEDSEP may be defined as a problem in which the cost of sys-em must be minimized, while its reliability must be maximizedhrough minimization of CIC.

An EDS may operate with other MECS with its nodes (energyubs). An energy hub may belong to customers or utility. Energy

nterchanging between an EDS and other MECS may change theystem’s resource scenarios, costs and reliability. Thus, the opti-al resource planning and operation of an energy interchanging

etween EDS and other MECS may be different from ordinary ones.lso, demand side management (DSM) programs and distributionutomation (DA) alternatives may significantly change the sys-em’s resources. Thus, DSM and DA alternatives must be taken intoccount in OEDSEP formulation.

Based on OEDSEP cost minimization and system reliabilityaximization function, the OEDSEP decision parameters can be

epresented in two related groups:

1. Investment and operation costs of distribution system resourcesand operational costs of the existing ones:• Investment and operational costs of new substations, feeders,

reactive power resources and utility owned generation devices,• Operation costs of the existing substation, feeders, reactive

power resources and utility owned generation devices inenergy hubs,

• Costs of the energy purchased from upward network,• Costs of the energy purchased from customer owned genera-

tion devices in energy hubs,• Investment and operation costs of DSM and DA programs,

. The reliability of distribution system decision variables, whichcan be represented as objective and constraints in OEDSEP. Inthis research, the reliability worth approach is used for OED-SEP. We propose that CIC can be considered as an objectivefunction in OEDSEP procedure, and that it must be minimizedbased on the first comprehensive distribution resource planningcriterion.

Systems Research 79 (2009) 899–911

OEDSEP determines how much investment (with correspondingoperational costs) is needed for maintaining of system reliabil-ity criteria. However, OEDSEP needs a slave problem known asoptimal device allocation and replacement problem (ODARP) todetermine where and when an investment is needed. For everyOEDSEP scenario, the ODARP problem must optimize the systemdevices characteristics and their allocation parameters for each yearof planning. However, assuming that the ODARP parameters arefixed and to maintain the system’s reliability criteria, the feasibil-ity of system restoration must be investigated. This problem is aslave problem of ODARP that uses the switching ability and optimalresource operational coordination under contingent conditions. Itis known as OR problem. Based on the above mentioned problemdescription and categorization, this research proposes a three-stagesolution algorithm. First, the OEDSEP solution is investigated, thenthe ODARP is solved for the fixed OEDSEP decision variables andfinally the OR is optimized for the fixed ODARP decision variables.

3. Problem modeling and formulation

The primary distribution network is presented by the balancedAC power flow model during all hours of year, and the generationand transmission constraints are represented as their correspond-ing volt–ampere limits, respectively [16]. The loads of a system arepresented as the load centers which must be supplied through pri-mary to secondary substations. The load flow model must considerpower generation at the system nodes. At the first stage of planningprocedure, the electric load of the system must be determined foreach year of planning horizon. Based on the land use load forecast-ing, the load centers are determined for the planning years.

The energy interchanging hubs can be classified as customerowned energy hubs (COEHs) and utility owned energy hubs(UOEHs). The COEH and UOEH in turn can be categorized as:

(1) Customer owned energy generating node, which can injectpower to an EDS. The customer owned energy generating hubmight be dispatchable by paying an appropriate capacity andenergy fee. The generation costs of a customer owned dispatch-able energy generating hub (CODEGH) can be formulated as(1):

CCODEGH = (˛CapDegh +Np∑i=1

ˇiPDeghgi

�i)� (1)

Some of the energy generating hubs may be nondispatch-able technically or economically. The power generation costof a customer owned nondispatchable energy generating hub(CONDEGH) can be formulated as (2):

CCONDEGH =(

Np∑i=1

�iPNDeghgi

�i

)� (2)

(2) Customer owned energy generating node, which can supplythe corresponding load and reduce its electric demand by therequest of utility. Thus, a node which can reduce its electricdemand by embedded electricity generation can be representedas dispatchable energy hub load (DEHL) or nondispatchableenergy hub load (NDEHL) based on the technical and economicparameters. The cost of load reduction in DEHL and NDEHL are
formulated as (3) and (4), respectively:
CDEHL =(�CapDehl +

Np∑i=1

�iPDehldi �i

)� (3)

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CNDEHL =(

Np∑i=1

ςiPNDehldi �i

)� (4)

It is assumed that the utility must negotiate with the ownersof CODEGH, CONDEGH, DEHL and NDEHL for power gener-ation/load reduction prices and profiles. A node, which canreduce its load by the request of utility and has not genera-tion capability, is categorized as DEHL or NDEHL. The utility canbilaterally contract with the selected energy hubs for energygeneration or load reduction. The failure rate and repair time ofCOEH, have to be taken into account in the reliability evaluationprocedure.

3) Utility owned energy generating nodes, which are using MECSfor power generation at primary distribution systems, havemultiple control variables for OEDSEP and ODARP. Their gen-eration capacity, location of installation and characteristics canbe considered as OEDSEP and ODARP control variables.

.1. OEDSEP problem formulation

Based on the problem description section, the objective functionf OEDSEP problem is proposed as (5):

inC1 =Nyear∑i=1

NSNzone∑j=1

[ ∑k∈PSNsub

∑l∈PSNtrans

CPSSub ijklϕPSSub ijkl

]

+Nyear∑i=1

NPNzone∑j=1

[ ∑k∈UPNsub

∑l∈UPNtrans

CUPSub ijklϕUPSub ijkl

+∑k∈PNFr

∑l∈PNfeed

CPNFeed ijklϕPNFeed ijkl +

∑k∈PNRPS

CPNRPS ijkϕPNRPS ijk

+∑

k∈PNUOEHCPNUOEH ijkϕ

PNUOEH ijk +

∑k∈UOPNEH

CPNCOEH ijkϕPNCOEH ijk

+∑

k∈PNDSMCPNDSM ijkϕ

PNDSM ijk +

∑k∈PNDA

CPNDA ijkϕPNDA ijk

+WCIC ×Noutage∑k=1

CICijk +Np∑k=1

ijkBEijk

](5)

The OEDSEP decision variables are binary cost (investment andggregated operating cost) variables. The OEDSEP minimizes theseosts as: present worth of investment cost of the new resourcesnd the operating cost of the new and the existing resourcesonsisting of primary to secondary network substations, upwardo primary network substations, primary network feeders, reac-ive power sources, utility owned energy hub generating devices,nergy cost purchased from COEH, investment and operation costsf DSM programs and DA alternatives. The OEDSEP objective func-ion considers energy purchased costs and weights the presentorth of customer interruption costs. We used the present worth

echnique for economic evaluation of the alternatives. Thus, theEDSEP objective function can be decomposed into three groups:

nvestment plus aggregated operation costs as the first group (eightentences of the OEDSEP’s objective function); CIC as the secondroup and finally, the costs of purchased energy as the third group.

The OEDSEP only deals with balancing between capital plus
perational costs and reliability worth costs. For fixed OEDSEPecision variables, the optimal location, type and capacity of theevices of new distribution resources and the corresponding invest-ent, operation and energy purchased costs must be determined inDARP. The parameters of (5) can be decomposed into the following
ystems Research 79 (2009) 899–911 901

items:

CSub = CSub fixed + CSub var (6)

CSub fixed = Ctrans inst + Ctrans nl loss (7)

Ctrans nl loss = (8760nl loss )� (8)

CSub var =(

8760 LSF(

SLTransSLloadcenter

)2

SCL

)� (9)

LSF = A(LF)2 + B LF, A+ B = 1 (10)

Eq. (6) denotes that the substation costs consist of fixed and vari-able costs. Fixed costs include transformer installation costs andtransformer no load loss costs. Variable costs include the trans-former nominal load energy loss costs.

CFeed = CFeed fixed + CFeed var (11)

CFeed fixed =nloadcen∑j=1

Cfeeder inst jLCj (12)

CFeed var = (8760 LSF 3RI2)� (13)

Eq. (11) denotes that the feeder costs can be decomposed intofixed and variable costs. The feeder fixed cost is equal to the feederinstallation cost. The feeder variable cost equals the annual energyloss costs. The OEDSEP objective function is subjected to many tech-nical and financial constraints, which have impact on the optimalityof the OEDSEP decision variables.

The technical constraints can be categorized into: network volt-age constraints, device loading constraints, the entire load centersto be served constraints, uniqueness parameter selection con-straints, and load flow constraint.

The uniqueness parameter selection constraints must include:new primary–secondary substation capacity selection, newupward–primary substation capacity selection, new primary net-work feeder selection and new primary network utility ownedgeneration capacity selection. The budget constraints must be takeninto account as a set of financial constraints. The load flow con-straints can be represented as:

f (x, u, z) = 0 (14)

where x, u, z are problem variables, controls and system topology,respectively. Technical, uniqueness parameter selection and budgetconstraints can be compactly represented as:

g(x, u, z) ≤ 0 (15)

3.2. ODARP problem formulation

At the second stage, for every OEDSEP decision variable set, theODARP problem must optimize the system’s device selection andallocation parameters. The ODARP must find the correspondinginvestment, operation and energy purchasing costs for each year.The ODARP objective function can be presented as (16):

MinCa2 =Nyear∑i=1

NSNzone∑j=1

[ ∑k∈PSNsub

∑l∈PSNtrans

CPSSub ijkl PSSub ijkl

]

+Nyear∑i=1

NPNzone∑j=1

[ ∑k∈UPNsub

∑l∈UPNtrans

CUPSub ijkl UPSub ijkl

+∑k∈PNFr

∑l∈PNfeed

CPNFeed ijkl PNFeed ijkl +

∑k∈PNDSM

CPNDSM ijk PNDSM ijk

9 ower Systems Research 79 (2009) 899–911

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+∑

k∈PNDACPNDA ijk

PNDA ijk +

∑k∈PNRPS

CPNRPS ijk PNRPS ijk

+PNefeed∑k=1

∑m∈PNFr

CPNFeed rep PNFeed rep ijkm

+UPesub∑k=1

∑m∈UPNtrans

CUPTrans rep ijkm UPTrans rep ijkm

+∑

m∈UOEH

NscUOEH∑n=1

CUOEH ijmn PN

+∑

m∈CODEGH

NscCODEGH∑n=1

CCODEGH ijmn PNCODEGH ijmn

+∑

m∈CONDEGH

NscCONDEGH∑n=1

CCONDEGH ijmn PNCONDEGH ijmn

+∑

m∈DEHL

NscDEHL∑n=1

CDEHL ijmn PNDEHL ijmn

+∑

m∈NDEHL

NscNDEHL∑n=1

CNDEHL ijmn PNNDEHL ijmn

](16)

It considers the following device allocation and capacity deter-ination or device replacement:Primary to secondary network substation, upward network to

rimary network substation, new primary feeder installation, DSMnd DA hardware installation, feeder and transformer replacement,OEH allocation and capacity selection, CODEGH, CONDEGH, DEHLnd NDEHL contribution selection.

The ODARP objective function is subjected to technical andnancial constraints. These constraints can be categorized into:etwork voltage constraints, device loading constraints, power bal-nce at the system nodes in normal and contingent conditions, thentire load centers to be served constraints, uniqueness parame-er selection constraints, radial operation of system in normal andontingent conditions, reliability constraints. Also, the uniquenessarameter selection constraints must be included as:

New primary–secondary substation capacity selection,ew upward–primary substation capacity selection, existingpward–primary network substation transformer replacement,xisting primary network feeder replacement, primary networkSM hardware allocation, primary network DA hardware alloca-

ion, primary network reactive power source allocation as wells COEH, DEHL and NDEHL contributions. Also, the reliabilityonstraints must be taken into account in the OR problem.

The optimal operational coordination of distribution resourcesf (16) must be considered for the fixed OEDSEP and ODARP decisionariables. This optimal operational coordination problem can beepresented as the operation cost minimization of UOEH, CODEGHnd DEHL:

MinCb2 (OPF ODARP)

=[PN UOEH∑

CPN +PN CODEGH∑

CPN

n=1OP UOEH ijmn

n=1OP CODEGH ijmn

+PN DEHL∑n=1

CPNOP DEHL ijmn

](17)

b εODARP fixed decision variables.

Fig. 1. Flowchart of proposed algorithms for solving of OEDSEP, ODARP and ORproblems.

ower Systems Research 79 (2009) 899–911 903

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This optimization problem must solve the optimal power gen-ration of UOEH and CODEGH and the optimal usage of DEHL forxed ODARP decision variables. The OPF problem can be solved byraditional OPF solving algorithms.

Technical and uniqueness parameter selection constraints cane presented as:

(x, u, z) ≤ 0 (18)

Load flow constraints are:

(x, u, z) = 0 (19)

.3. OR problem formulation

For a fixed ODARP decision variables set, the optimum topol-gy and device characteristics of system are recognized [17,18]. TheR problem tries to find the optimal operational coordination of

ystem devices in contingent conditions. However, the control vari-bles of the distribution system under restoration conditions can beategorized as:

1. Discrete control variables of system resources such as switchingof tie switches and capacitor banks, and

. Continuous control variables of system resources such as UOEH,CODEGH, DEHL.

For a fixed ODARP, tie switches are allocated and switch-ng of them can change the distribution system resources. Alsoor a specified capacity of RPS in a fixed system topology, theptimum capacitor steps must be determined in restoration pro-edure. The OR problem must take into account the distributionesources, which may behave as continuous variables such asctive and reactive powers or voltages of utility/customer ownedenerating hubs, or discrete variables such as switching of theapacitors and lines. Discrete control variables such as line switch-ng can generate new distribution resources. Optimal restorationroblem consists of switching of tie switches and capacitorsor a fixed ODARP decision variable set and needs a searchlgorithm.

The OR objective function is proposed as:

MinCb3 =Nyear∑i=1

Nzone∑j=1

Noutage∑k=1

NLoad∑m=1

CICijkm

CICm = [WF1m × C(d1m) × L1m +WF2m

×{C(d2m) − C(d1m)} × L2m +WF3m

×{C(d3m) − C(d2m)} × L3m + . . .+WFkm×{C(dkm) − C(d(k−1)m)} × Lkm]

(20)

εOPSEP fixed decision variable set, Lk is average load demandnterrupted during hour k (MW), WFk is cost weight fac-or at hour k. C(dk) is interruption cost at hour k fromhe composite customer damage function of bus (monetarynit/kW).

The OR procedure investigates the adequacy of system resourcesor restoration of the most important loads. Then, it tries to switchhe tie switches and capacitors and find a new set of systemesources. For new topology, the optimal coordination of contin-ous control variables’ problem can be solved by OPF, and itsbjective function can be represented as: Ta

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Power

Systems

Research79

(2009)899–911

Table 2The generation energy hub (CODEGH and CONDEGH) and load reduction (DEHL and NDEHL) information.

#B Period Year 1 Year 2 Year 3 Year 4 Year 5Scen1 Scen2 Scen3 Scen1 Scen2 Scen3 Scen1 Scen2 Scen3 Scen1 Scen2 Scen3 Scen1 Scen2 Scen3

21 ˛ 1 3.2 4 7.1 3.45 4.6 8.05 4.002 5.336 9.338 4.762 6.349 11.11 5.667 7.556 13.222 7.4 9.2 10.2 8.05 10.35 11.5 9.338 12.00 13.34 11.11 14.28 15.87 13.22 17.00 18.89

ˇ 1 75.5 71.1 67.3 86.25 81.65 77.05 100.0 94.71 89.37 119.0 112.7 106.3 141.6 134.1 126.52 74.2 72.2 71.1 86.58 84.24 83.07 102.1 99.40 98.02 121.0 117.7 116.1 143.4 139.5 137.6

44 ˛ 1 7.6 5 4.5 8.19 5.85 5.265 9.664 6.903 6.212 11.45 8.180 7.362 13.57 9.693 8.7242 9.1 7.5 6.5 10.53 8.19 7.605 12.42 9.664 8.973 14.72 11.45 10.63 17.44 13.57 12.60

ˇ 1 80.2 88.5 89 95.2 104.7 105.9 114.2 125.6 127.0 150.7 165.8 167.7 199.0 218.9 221.42 78.6 83.1 85 92.82 98.77 101.1 111.3 118.5 121.3 147.0 156.4 160.2 194.0 206.5 211.4

4 ˛ 1 2.5 3.5 5.5 2.975 4.165 6.545 3.57 4.998 7.854 4.712 6.597 10.36 6.220 8.708 13.682 3.4 5 6.3 3.63 6.05 7.26 4.428 7.381 8.857 5.624 9.373 11.24 7.142 11.90 14.28

ˇ 1 84.3 81.2 80.1 101.6 98.01 96.8 124.0 119.5 118.0 157.4 151.8 149.9 200.0 192.8 190.42 81 77.5 75 98.01 93.17 90.75 119.5 113.6 110.7 151.8 144.3 140.6 192.8 183.3 178.5

1 � 1 52.3 50.8 48.1 59.8 57.5 55.2 69.66 66.98 64.30 81.85 78.71 75.56 97.82 94.05 90.292 65.1 70.2 74.6 74.75 80.5 85.1 87.08 93.78 99.14 102.3 110.1 116.4 122.2 131.6 139.2

41 � 1 45.5 55.9 61.3 53.32 65.17 72.28 63.72 77.88 86.38 76.46 93.46 103.6 98.64 120.5 133.72 70.4 65.2 62.4 82.95 77.02 73.47 99.12 92.04 87.79 118.9 110.4 105.3 153.4 142.4 135.9

9 � 1 12.3 14 15 14.28 16.66 17.85 16.99 19.82 21.24 20.22 23.59 25.28 24.06 28.07 30.082 15 17 18 17.85 20.23 21.42 21.24 24.07 25.48 25.28 28.65 30.33 30.08 34.09 36.09

� 1 125.8 118 110.5 148.7 140.4 130.9 177.0 167.0 155.7 210.6 198.8 185.3 250.6 236.6 220.52 130 125 129 154.0 148.1 152.8 182.5 175.5 181.1 201.7 193.9 200.1 222.9 214.3 221.1

13 � 1 8.5 6 7 9.48 7.11 8.295 11.23 8.425 9.829 12.41 9.31 10.86 13.71 10.29 122 11.4 7 9 13.03 8.295 10.66 15.44 9.829 12.63 17.07 10.86 13.97 18.86 12 15.44

� 1 111 108 115.7 146.5 142.5 151.8 193.4 188.1 200.3 216.6 210.7 224.4 242.6 236.0 251.32 116.5 110 108 153.1 145.2 142.5 202.1 191.6 188.1 226.3 214.6 210.7 253.5 240.4 236.0

51 � 1 14 11.4 9.5 18.48 14.52 11.88 24.39 19.16 15.68 27.32 21.47 17.56 30.6 24.05 19.672 17 16 15 23.29 21.92 20.55 31.90 30.03 28.15 37.33 35.14 32.94 43.68 41.11 38.54

� 1 119.4 114 121.5 163.0 156.1 165.7 223.3 213.9 227.1 261.3 250.3 265.7 305.7 292.9 310.82 124 129 135 169.8 176.7 184.9 232.7 242.1 253.3 272.3 283.2 296.4 318.5 331.4 346.8

39 � 1 10.7 8 7 11.7 9.36 8.19 13.68 10.95 9.582 17.39 13.91 12.17 22.09 17.67 15.462 11.5 15.5 5 13.45 18.13 5.85 15.74 21.21 6.844 19.99 26.95 8.69 25.39 34.23 11.04

� 1 121.5 127.5 145 141.5 148.5 169.6 165.6 173.8 198.4 210.3 220.7 252.0 267.1 280.4 320.12 135 139 149.1 144.4 148.7 159.4 154.5 159.1 170.5 165.3 170.2 182.5 176.9 182.2 195.3

24 � 1 8.7 9.3 5.7 8.56 9.63 5.35 9.159 10.30 5.724 9.8 11.03 6.13 10.49 11.8 6.562 11 12 15 11.77 12.84 16.05 12.59 13.73 17.17 13.48 14.7 18.38 14.42 15.73 19.67

� 1 119 115.4 122 130.9 126.5 134.2 143.9 139.1 147.6 158.3 153.0 162.3 174.2 168.3 178.62 123 119 132.7 135.3 130.9 145.2 148.8 143.9 159.7 163.7 158.3 175.6 180.0 174.2 193.2

54 � 1 12 10 9 13.2 11 9.9 14.52 12.1 10.89 15.97 13.31 11.98 17.57 14.64 13.182 14.4 12 15 16.38 14.04 17.55 19.16 16.43 20.53 22.42 19.22 24.02 26.23 22.49 28.1

� 1 131 135 139.7 153.2 157.9 162.6 179.3 184.8 190.2 209.8 216.2 222.6 245.4 252.9 260.42 138 141 121 161.4 164.9 141.5 188.9 193.0 165.6 221.0 225.8 193.8 258.5 264.2 226.7

19 � 1 32.3 28.6 25.5 38.44 34.03 30.35 45.74 40.5 36.12 54.43 48.2 42.98 64.77 57.36 51.152 135.1 141.2 146.8 160.7 168.0 174.6 191.3 199.9 207.8 227.6 237.9 247.3 270.9 283.1 294.3

40 � 1 47.2 45.6 43.3 52.16 50.39 47.85 57.64 55.68 52.87 63.69 61.53 58.42 70.38 67.99 64.552 152 162.3 132 167.9 179.3 145.8 185.6 198.1 161.1 205.0 218.9 178.1 226.6 241.9 196.8

50 � 1 36.6 31.2 28.2 42.09 35.88 32.43 48.4 41.26 37.29 55.66 47.45 42.88 64.01 54.57 49.312 142.1 153.1 165.1 163.4 176.0 189.8 187.9 202.4 218.3 216.1 232.8 251.0 248.5 267.7 288.7

M.S.N

azar,M.R

.Haghifam

/ElectricPow

erSystem

sResearch

79(2009)

899–911905

Table 3Final ODARP transformer allocation results.

#B Zone # Loadcenter

Load in2007 (KVA)

Existingsubstationscapacity (KVA)

Load in2008 (KVA)

First year(2008)capacity(KVA)

Load in2009 (KVA)

Second year(2009)capacity(KVA)

Load in 2010(KVA)

Third year(2010)capacity(KVA)

Load in 2011(KVA)

Fourth year(2011)capacity(KVA)

Load in2012 (KVA)

Fifth year(2012)capacity(KVA)

1 1 L1-1 111.09 200 117.79 200 124.88 200 132.29 200 139.86 200 148.17 2002 1 L1-2 287.39 400 304.71 500 323.04 500 342.2 500 363.11 500 383.28 6303 1 L1-3 115.13 200 122.07 200 129.41 200 137.08 200 144.93 200 153.54 2504 1 L1-4 232.89 315 247.03 400 261.89 400 277.42 400 293.3 400 310.72 5005 1 L1-5 178.7 250 189.5 315 200.91 315 212.83 315 225.01 315 238.37 4006 1 L1-6 288.92 400 306.38 500 324.82 500 344.08 500 363.78 500 385.38 5007 1 L1-7 275.74 400 292.34 400 309.93 500 328.32 500 347.11 500 367.72 5008 1 L1-6 – – – – – – – – 45.11 100 146.12 2009 2 L2-1 48.49 100 51.42 100 54.51 100 57.75 100 61.05 100 64.68 10010 2 L2-2 200 315 212.09 315 224.84 315 238.18 400 251.82 400 266.78 40011 2 L2-3 74.3 100 78.74 200 83.47 200 88.42 200 93.47 200 99.04 20012 2 L2-4 257.64 400 273.12 400 289.55 400 306.72 500 324.28 500 343.54 50013 2 L2-5 199.13 315 211.17 315 223.88 315 237.16 400 250.72 400 265.62 40014 2 L2-6 288.87 400 306.38 500 324.82 500 344.08 500 363.78 500 385.38 63015 2 L2-7 143.67 200 152.28 250 161.43 250 171.01 250 180.79 250 191.54 25016 2 L2-8 314.83 500 333.86 500 353.93 500 374.46 500 396.39 500 419.93 50017 2 L2-8 – – – – – – 61.2 100 149.1 200 143.12 20018 2 L2-7 – – – – – – – – – – 67.12 10019 3 L3-1 737.33 1000 781.99 1250 829.03 1250 878.21 1250 928.47 1250 983.62 160020 3 L3-2 534.54 800 566.84 800 600.93 1000 636.59 1000 673.03 1000 713 100021 3 L3-3 615.74 1000 617.45 1000 692.33 1000 733.41 1000 775.39 1000 821.43 100022 3 L3-4 53.2 100 56.45 100 59.86 100 63.41 100 67.03 100 71.01 10023 3 L3-5 260.87 400 276.63 400 293.28 400 310.67 500 328.45 500 347.96 50024 3 L3-6 35.16 50 37.22 50 39.47 100 41.82 100 44.21 100 46.83 10025 3 L3-3 – – – – – – – – 187.1 315 235.65 31526 3 L3-3 – – – – – – – – 46.2 100 62.1 10027 3 L3-6 – – – – – – 27.13 50 43.31 100 56.32 10028 4 L4-1 335.11 500 355.37 500 376.74 630 399.09 630 421.93 630 446.99 63029 4 L4-2 28.09 50 29.75 50 31.54 50 33.42 50 35.33 50 37.42 5030 4 L4-3 243.7 400 258.47 400 274.03 400 290.28 400 306.89 500 325.12 50031 4 L4-4 24.21 50 25.63 50 27.17 50 28.79 50 30.43 50 32.24 5032 4 L4-5 196.05 315 207.97 315 220.47 315 233.57 315 246.93 315 261.59 31533 4 L4-6 64.79 100 68.66 100 72.79 100 77.11 200 81.53 200 86.37 20034 4 L4-7 150 200 159.14 250 168.71 250 178.72 250 188.95 250 200.18 25035 4 L4-5 – – – – – – – – 69.12 100 73.12 10036 4 L4-7 – – – – – – – – 61.23 100 75.14 10037 5 L5-1 – – – – – – – – – – 241.23 31538 5 L5-1 460.53 630 488.42 800 517.79 800 548.51 800 579.91 800 614.34 100039 5 L5-2 287.89 400 305.32 400 323.68 500 342.89 500 362.51 500 384.04 63040 5 L5-3 766.86 1250 813.26 1250 862.18 1250 913.34 1250 965.62 1600 1022.96 160041 5 L5-4 827.22 1250 877.63 1250 929.96 1250 985.13 1250 1041.53 1250 1103.42 125042 5 L5-5 346.41 500 367.42 500 389.51 630 412.63 630 436.25 630 462.16 80043 5 L5-6 191.36 315 202.93 315 215.14 315 227.91 315 240.95 400 255.26 31544 5 L5-7 121.04 200 128.32 200 136.04 200 144.12 200 152.37 250 161.41 25045 5 L5-8 130.64 200 138.54 200 146.88 200 155.59 250 164.5 250 174.26 25046 5 L5-4 – – – – – – 125.1 200 136.41 200 145.2 20047 5 L5-4 – – – – – – 129.65 200 141.12 200 149.6 20048 5 L5-4 – – – – – – 31.2 50 65.2 100 71.2 10049 6 L6-1 – – – – – – – – 236.74 315 264.23 31550 6 L6-1 612.83 1000 649.84 1000 688.93 1000 729.82 1000 771.58 1000 817.41 100051 6 L6-2 422.92 630 448.43 630 475.41 630 503.62 800 532.45 800 564.07 80052 6 L6-3 367.71 500 390 630 413.46 630 437.99 630 463.07 630 490.57 63053 6 L6-4 450.33 630 477.58 800 506.32 800 536.36 800 567.05 800 600.72 80054 6 L6-5 566.68 800 601.03 1000 637.17 1000 674.97 1000 713.62 1000 755.99 100055 6 L6-3 – – – – – – – – – – 274.23 31556 6 L6-5 – – – – – – – – – – 281.74 315

Load factor .75 .78 .79 .809 .827 .852Energy consumption (kWh) 77,841,360 85,608,151.2 92,179,728 98,882,179.2 107,166,160.8 117,676,584

9 ower

c

l

r

y

a

f

4

petttevss(o

MaxQ1 =M1 − C1 −W1g(u, x, z) −W2f (u, x, z) (25)

TF

C

N

P

D

D

RT

TP

PEEETE

E

E

E

E

E


MinCc3(OPF OR)

=Nyear∑i=1

Nzone∑j=1

Np∑k=1

{( ∑m∈UOEH

�CPNOP UOEH ijkm

+∑

m∈CODEGH�CPNOP CODEGH ijkm +

∑m∈DEHL

�CPNOP DEHL ijkm

)}(21)

εODARP and OR fixed decision variable set.The third stage optimization constraints are voltage drop, line

oading and load flow constraints.Technical and radial operation constraints can be compactly rep-

esented as:

′n(x, u, z) ≤ 0∀n∈ {0,1, . . . , Noutage} (22)

Load flow constraints, which use the detailed load flow model,re formulated as:

′n(x, u, z) = 0∀n∈ {0,1, . . . , Noutage} (23)

. Solution algorithm

The mentioned model of OEDSEP is a mixed integer nonlinearrogramming (MINLP) problem. The ODARP subproblem is nonlin-ar and nonconvex, if the OEDSEP decision variables are fixed. Thehird subproblem uses discrete control variables such as capaci-or and line switching. It generates new state spaces for solvinghe optimal restoration OPF. Every effective switching will gen-rate a new state space with its new outages based on the load
ariations. The three-stage problem has a great state space and itsolution algorithm must have the ability to effectively search thispace. For optimization procedure, we propose genetic algorithmGA) with variable fitness functions. Fig. 1 depicts the flowchartf the proposed multistage optimization algorithm. First, the OED-
able 4inal ODARP parameter selection results.

osts First year

ew substation and transformer replacement costs(1000 Rials)

461,141

rimary network new line installation and replacement costs(1000 Rials)

–

emand side management resources costs (DSM programstarts at first year) (1000 Rials)

70,726

istribution automation resources costs without switchingdevice costs (DA program starts at first year) (1000 Rials)

1,015,500

eactive power resources costs (1000 Rials) 1,17,259otal switching device installation and replacement cost fromDA resources (1000 Rials)

12,350

otal installation and replacement costs (1000 Rials) 1,676,976ercent of not supplied energy (in corresponding year) withoutresource procurement

6.6

ercent of aggregated not supplied energy 6.6nergy purchased from upward network (kWh/year) 50,542,933.3nergy purchased from COEH (kWh/year) 3,879,617.9nergy purchased from UOEH (kWh/year) 31,185,600otal energy purchased from EH (kWh/year) 35,065,217.9nergy generation of CODEGH 4 (kWh/year) (DeutzTBG632V16/3260 kW) at PN

25,70,200

nergy generation of CODEGH 44 (kWh/year) (DeutzTCG2032V12/2300 kW capacity but 1600 kW dispatchable)at PN

946,169.9

nergy generation of CODEGH 21 (kWh/year) (DeutzTBG632V16/3000 kW capacity but 800 kW dispatchable) atPN

3,63,144

nergy generation of UOEH (kWh/year) (DeutzTBG632V16/4000 kW) at PN at bus 40

31,185,600

nergy generation of CONDEGH 1 (kWh/year) (DeutzGBF6L913C/power 89 kW) at secondary side of PS substation

104.21

nergy consumption (kWh) 85,608,151.2


SEP problem is optimized for each year of the planning years fordistribution system. Then, an ODARP is solved and finally, theOR subproblem is optimized to investigate a feasible and optimalrestoration problem solution.

4.1. OEDSEP optimization algorithm

In order to map the possible solutions of the OEDSEP problem, abinary basis codification is employed. Coding of the decision vari-ables into chromosomes may produce impractical input data. Thus,at the coding stage, any impractical input data will be removed fromthe search space. The set of decision variables, which are selectedas chromosomes, may be represented as:

GAP1 = [ϕPSSub, ϕUPSub, ϕ

PNFeed, ϕ

PNUOEH,ϕ

PNCOEH,ϕ

PNRPS, ϕ

PNDSM,ϕ

PNDA] (24)

Eq. (24) denotes the decision variables. To improve the perfor-mance and speed of the specified GA, a list of suitable candidatesis selected for the first generation of the chromosomes. This popu-lation could be generated using engineering experience rules. Twooperators, named as crossover and mutation, are applied to the firstgeneration and new chromosomes are generated. The crossoveroperator is employed for each of the decision variables. For imple-mentation of operational constraints in the optimization process, apenalty factor representation is used and when an infeasible solu-tion violates the constraints, the corresponding penalty factors areimposed on the first stage objective function. The final optimizationfitness function of the OEDSEP problem can be proposed as:

where M is a high number and W is weight factor. Weight factorsare increased linearly through iterations from zero to a very highnumber. A set of optimal or sub-optimal populations are used forthe second stage problem.

Second year Third year Fourth year Fifth year

132,879 375,858 325,593 312,545

– 50,278 63,629 100,679

4465 4454 4572 4996

93,990 12,985 69,300 30,750

19,788 288 316 316890 1780 1780 890

252,012 445,643 465,190 450,1765.5 5.7 7.3 8.93

12.1 17.8 25.1 34.0357,610,525.3 65,077,725.93 74,005,166.1 85,061,632.13,527,266.7 3,319,653.27 3,026,594.7 2,830,951.931,041,936 30,484,800 30,134,400 29,784,00034,569,202.7 33,804,453.27 33,160,994.7 32,614,951.92,432,200 2,356,000 2,170,400 2,060,900

749,922.7 682,165.27 614,418.7 553,679.9

345,080 281,430 241,723 216,324

314,041,936 30,484,800 30,134,400 29,784,000

64.58 58.73 53.67 48.08

92,179,728 98,882,179.2 107,166,160.8 117,676,584

ower S

4

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Fs


.2. ODARP optimization algorithm

The ODARP problem is proposed using binary basis codificationrocedure. The ODARP decision variables set which are selected ashromosomes may be represented as:

AP2 = [ PSSub, UPSub,

PNFeed,

PNDSM,

PNDA,

PNFeed rep,

UPTrans rep,

× PNRPS, PNUOEH, PNCODEGH, PNCONDEGH, PNDEHL, PNNDEHL] (26)

By starting with a set of optimal or sub-optimal solutions of therst stage optimization, the final optimization fitness function ofhe ODARP problem can be presented as:

axQ2 =M2 −W3Ca2 −W4C

b2 −W5h(u, x, z) −W6f (u, x, z) (27)

here M is a high number and W is weight factor. A set of optimalr sub-optimal populations are used for the third stage problem.

.3. OR optimization algorithm

For optimal restoration of the network for a specified topology,e propose GA in order to find an optimal tie switch and capacitor

witching alternatives, which their discrete variables are selecteds:

bi = 1 if the ith element shunt is used, else it equals 0 (28)

Si = 1 if the ith tie switch is used, else it equals 0 (29)

Thus, the GA population is:

AP3 = [Cb1, TS1, . . . , CbNSRPS+NPRPS, TSNTS]

Direct coding of the discrete variables to the chromosomes willroduce a large number of impossible GA input data. Searchinghe space of GA must have no impossible coded data; otherwiseny impossible generated data will be removed from search set.or implementation of the operational constraints in an optimiza-
ion process, they will be presented as penalty factors. The finalptimization fitness function of an OR problem can be proposeds:
axQ3 =M3 −W7Cb3 −W8C

c3 −W9

→y′(u, x, z)−W10

→f ′(u, x, z) (30)

ig. 2. Cost of energy generation or load reduction at CODEGH, CONDEGH, DEHL, and NDcenarios.

ystems Research 79 (2009) 899–911 907

The stopping criteria of the algorithm can be a reliabilityindex or a mismatch checker, which terminates the optimizationprocedure.

5. Numerical results

The proposed algorithm was applied to an urban electric distri-bution system. This network was a part of a city EDS with 30,000customers at the horizon year of 2012. The selected region will haveabout 4500 residential and commercial customers at the horizonyear, and its primary network voltage is 20 kV and its secondarynetwork voltage is 400 V, respectively. At first, the load forecast-ing procedure was preceded for distribution system and the loadcenters of primary network was determined. Using a land use loadforecasting procedure, a load growth scenario was considered forthe system. The percentage of residential and commercial con-sumptions at each load center was determined for the planningyears. The time horizon was chosen the year 2012, or 5 year intothe future. Accordingly, the peak load for the system will grow from11.848 to 15.804 MW. Substantial shortages and significant unrelia-bility will be present in the system unless new resources are added;even if all the required equipments were fully available. In the hori-zon year, totally 34.03% of the annual energy demand would beunserved if no new resources are added.

A set of primary network resource investments is available forinstallation by the year 2012. The DSM and DA programs as resourcealternatives are shown in Table 1 in terms of monetary units (Rial),respectively. The reactive power resources are delta connected,which their installation and O&M costs are 19,500 Rial per Kvar and750 Rial per Kvar year.

For calculation of CIC, four approximate methods are avail-able: event-based customer interruption cost evaluation (EBCIC),average demand interrupted approach (ADIA), average deliverypoint restoration duration approach (ADPRD) and average systemrestoration duration approach (ASRD).

In the present research, we used the ADIA. This approximatemethod relies on the single event-based concept. The average load
curtailment (average demand interrupted) can be calculated as theratio of the unserved energy during an interruption to the dura-tion of the interruption. The composite customer damage function(CCDF) of each load bus is formed using the sector customer dam-age functions (SCDF) allocated at that bus weighted by their sector
EHL for different years of planning with their corresponding selected contribution

9 ower

aii

icnCmtnsnwhaiaootOmffnpOraataf


nnual energy consumptions. The CCDF formed for each load buss, therefore, a fixed function, and is calculated at the beginning asnput data before starting the simulation [19,20].

Primary feeder replacement costs are 0.7 of the correspond-ng feeder installation cost. In addition, transformer replacementost is 0.45 of the corresponding transformer replacement cost. Byegotiation with the customer owned energy hubs, the CODEGH,ONDEGH, DEHL and NDEHL groups are recognized. The utilityust investigate the optimum energy generation and load reduc-

ion of its nodes at normal and restoration conditions of theetwork. The capacity fee, power generation fee and contributioncenarios of CODEGH, CONDEGH, DEHL and NDEHL for the comingext years are shown in Table 2. The upward network energy priceill be 62, 65, 69.1, 72, 76 for the first to fifth years of the planningorizon, respectively, as calculated by market simulator. The UOEHllocation buses are all of primary network buses and their spec-fications are traditional medium voltage generation units, suchs Vartsilla and Deutz (data are not shown). The reliability dataf UOEH and COEH are considered in reliability calculations. Theperational costs of UOEH and COEH are extracted from the men-ioned energy generating devices and considered in ODARP OPF. TheEDSEP, ODARP and OR codes are developed on MATLAB 7.1. Theirodules are written by C++ language and these modules have inter-

aces with MATLAB 7.1 by DLLs. The load flow model of system, usedor OEDSEP problem, considers the power injection at the systemodes and simply calculates the voltage drop to consider OEDSEProblem constraints. However, the load flow model of ODARP andR problems must consider the detailed model of EDS. By catego-

izing of the fault types to permanent and momentary faults, the
verage failure rate and the repair time of each category of faultsre calculated, based on the reliability database. The average repairime of the momentary faults with and without DA is assumed as 3nd 10 min, respectively. The average repair time of the permanentaults is assumed as 3 h and the weight factor for all the loads is
Fig. 3. Sensitivity analysis for energy price change impact on COD


1.0. In this paper, we used the weighted average system reliabilityindex (WASRI) for stopping criteria, defined as:

WASRI = wf ′1 × SAIDI +wf ′2 × SAIFI +wf ′3 ×MAIFIE (31)

where

SAIFI = total number of customer interruptionstotal number of customers served

. (32)

SAIDI = sum of customers’ interruption durationtotal number of customers

. (33)

Momentary interruption refers to a state of zero voltage of acomponent that lasts not more than 5 min [21]:

MAIFIE = iTiN

(int/year customer) (34)

where Ti is the number of customers experiencing temporary inter-ruption event to failure of component i, N is the total numberof customers and wf ′ is the weight factor. The stopping criterionwas selected as WASRI < 8 for all the buses based on the utilityreliability standard with wf ′1 = wf ′2 = wf ′3 = 1/3 or the number ofiterations > 1000.

The planning model was run for the horizon year with the distri-bution system resources and the OEDSEP parameters are: crossoverprobability = 0.75, mutation probability = 0.65, Np = 2 (peak and offpeak), acceptable voltage drop = 3%.

The computational time for GA on a Pentium IV 2.4-GHzpersonal computer was about 295 min. The OEDSEP investmentand aggregated operation costs (8 out of 10 parts of OEDSEPobjective function), take on a value of 3289.997 million Rials,
which are decomposed into 1676.976, 252.012, 445.643, 465.190,450.176 million Rials for the first, second, third, fourth and fifthyears of the planning, respectively. The rate of return parameterwas considered as 1.05%. The optimal OEDSEP resource selectionsare: RTI RDS = 41 (all of the system load centers), CSTI RDS = 41,
EGH energy generation costs for different years of planning.

ower S

Cc

trcoutfnwsitwsfrDf


FL SP = 41, EEM = 41, SIED = 56 (all of PS substation), AMR (41 loadenters).

The ODARP problem determines the optimum allocation of sys-em substations and feeder routing as well as the existing deviceeplacement. The optimum investment and aggregated operationosts, CIC and energy purchased costs are referred from ODARP toptimal OEDSEP decision variables. The ODARP problem consists ofsing of the existing devices, which their life cycle is not expired, buthe devices with greater capacity are needed. For example, a trans-ormer, which supplies a load, but the capacity of transformer isot enough for supplying the load, must be changed by another oneith greater capacity. The older transformer can be used in other

ubstations to supply an appropriate load. This problem was takennto account in ODARP procedure. Table 3 shows the final ODARPransformer allocation results, in which the mentioned problemas considered. As shown in Table 3, the initial load factor of the

ystem, without DSM impacts is 0.75, but DSM will improve its load
actor from 0.75 to 0.852 at 2012. Table 4 depicts the final ODARPesults for new substations, new line installations or replacements,SM and DA alternatives and RPS costs. New substation and trans-
ormer installation costs are uniformly distributed in the planning

Fig. 4. Optimal primary network topology and its corr

ystems Research 79 (2009) 899–911 909

years. However, the primary network’s new installation costs arepostponed until the third year of planning, because of adequacy ofthe existing primary network. DSM, DA, and RPS alternatives areheavily used for increasing of the load factor and system reliabilityand also for decreasing of the reactive power loss, because none ofthem are implemented in the existing system before OEDSEP. Thecustomer interruption costs increase for the fourth and fifth years,because of rapid load growth at these years, if no system resourcesare available at the corresponding years. The optimal allocation ofUOEH is at bus 40, which is a 4000 kW power generation device atthe primary network.

Fig. 2 depicts the costs of energy generation or load reductionat CODEGH, CONDEGH, DEHL, and NDEHL for different years ofplanning. As shown, the NDEHL9 is the most expensive load reduc-tion alternative. The sensitivity analysis is performed to analyze theimpact of energy price on the energy generation costs of CODEGHfor the planning years. Fig. 3 shows the results of this analysis and
the maximum change in the energy generation costs is 0.9% forDGEH44, which means that small changes in the energy prices ofDGEH will not change the optimality of the DGEH energy generationscenarios.
esponding WASRI at fifth year of planning years.

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The OR problem investigates the adequacy of system resourcesor restoration of the most important loads. Then, it tries to switchhe tie switches and capacitors and find a new set of systemesources. The final and optimum topology of the system has 462-ndependent single short and long time failures. Based on the ORrocedure, the stopping criterion is selected as WASRI, a sample ofhich is shown in Fig. 4 for the optimized system topology at thefth year of planning. As Fig. 4 shows, the maximum WASRI indexakes on a value 7.566 at bus 22. Based on the mentioned WASRIalculation, the optimal system topology of the system is shownn Fig. 4. The optimal tie switch allocations are also illustrated inhe figure. The tie switches include YASKAWA sectionalizers, whichre equipped with RTU that works according to DNP3/IEC 870--101 protocol. Under normal conditions, the sectionalizers areormally opened and can be used for optimal restoration and their

mpedances are zero.

. Conclusion

A new optimal electric distribution resource planning (OED-EP) procedure, which considers the optimal device allocationnd replacement problem (ODARP), was proposed in the presentesearch. Also an integrated formulation of the distributionesource and expansion planning based on the demand sideanagement, distribution automation alternatives and customer

wned facilities was presented. In addition, the utility ownednergy hub generation devices were optimally allocated.

The contribution of the present research can be summarized asollows:

1. It represents a new OEDSEP and ODARP formulation, which isable to find the optimum usage of MECS and other distributionresources for minimizing the utility costs and maximizing itsreliability. It considers the impact of MECS on the energy pro-curement scenarios and the reliability indices of EDS.

. It considers the optimal restoration subproblem for optimalEDS resource coordination under contingent conditions. It usesthe optimum switching procedure for searching of all systemresources in the restoration subproblem.

ased on a three-stage optimization procedure, the OEDSEP andDARP are solved simultaneously. The proposed algorithm con-

iders the OR subproblem, which optimizes the system resources’,perational coordination under restoration conditions. The algo-ithm was tested for an urban electric distribution systemxpansion planning in 2012 with quite acceptable results. Theuthors are investigating an alternative algorithm for speeding upptimization calculations.

ppendix A. List of symbols

E total energy purchased from upward networkapDehl load reduction capacity prepared by DEHLSub present worth of new substation costsFeed present worth of feeder costsUOEH present worth of UOEH costsCOEH present worth of COEH costsDSM present worth of demand side management (DSM) alter-

natives costs
DA present worth of distribution automation (DA) alterna-
tives costsRPS present worth of reactive power resource (RPS) alterna-

tives costsFeed rep present worth of existing feeder replacement costs


CTrans rep present worth of existing transformer replacement costsCtrans nl loss present worth of transformer no load loss costCTrans inst present worth of transformer installation costCFeeder inst present worth of feeder installation costCCODEGH present worth CODEGH contribution costCCONDEGH present worth of CONDEGH contribution costCDEHL present worth of DEHL contribution costCapDegh generation capacity prepared by DEGHLF load factorLC load center distance from corresponding substationLSF loss factornl loss no load loss of transformernloadcen number of primary network load centersNb number of system busesNyear number of planning yearsNoutage number of primary network outagesNp number of load curve periodsNSNzone number of secondary network zonesNPNzone number of primary network zonesPDeghg DEGH active power generation

PNDeghg NDEGH active power generationPDehld

DEHL active load reductionPNDehld

NDEHL active load reductionPD active loadPUnserved not served active loadPOut active load flow from busPNFr number of primary network feeder type alternatives for

installationPN primary networkPNfeed number of PN feeder section routing alternativesPNEH PN energy hub contributing candidatesPNRPS number of PN RPS utilization candidatesPNDSM number of PN DSM utilization candidatesPNDA number of PN DA utilization candidatesPNefeed number of existing PN feeder sectionsPNNb number of PN busesPNUOEH UOEH at PNUPNsub number of UP substations alternativesUPNtrans number of UP network transformer alternativesQD reactive loadR equivalent circuit resistanceSCL short circuit loss of transformerSLTrans transformer nominal loadSLloadcenter load center nominal loadPSNsub number of PS substations alternativesPSNtrans number of PS network transformer alternativesPSesub number of PS existing substationT number of equivalent annual peak hoursUP upward–primary network substationUPesub number of UP existing substation

Greek letters˛ generation capacity fee of DGEHˇ energy generation fee of DGEH� load reduction option fee of DEHL� load reduction energy fee of NDEHL electricity price� present worth factorς load reduction fee of DEHL� time duration of EH contribution
ϕEH decision variable of EH contributionϕSub decision variable of substation installationϕFeed decision variable of feeder installationϕDSM decision variable of DSM utilization alternativeϕDA decision variable of DA utilization alternative

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Mahmood Reza Haghifam received the B.S. degree from Tabriz University, Iran and


RPS decision variable of RPS utilization alternativeUOEH decision variable of UOEH utilization alternativeCOEH decision variable of COEH utilization alternative

load reduction energy fee of DEHLFeed rep decision variable of feeder replacementTrans rep decision variable of transformer replacementSub decision variable of substation allocationFeed decision variable of feeder allocationRPS decision variable of RPS alternatives allocationDA decision variable of DA alternatives allocationDSM decision variable of DSM alternatives allocationCODEGH decision variable of CODEGH contributionCONDEGH decision variable of CONDEGH contributionUOEH decision variable of generating UOEH contributionDEHL decision variable of DEHL contributionNDEHL decision variable of NDEHL contribution

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[2] M. Geidl, G. Andersson, A modeling and optimization approach for multipleenergy carrier power flow, in: Proc. IEEE Power Eng. Soc. PowerTech, St. Peters-burg, Russian Federation, 2005.

[3] G.L. Thompson, D.L. Wall, A branch and bound model for choosing optimalsubstation locations, IEEE Trans. Power App. Syst. 100 (1981) 2683–2687.

[4] M.R. Haghifam, M. Shahabi, Optimal location and sizing of HV/MV substationin uncertainty load environment using genetic algorithm, Electric Power Syst.Res. 63 (2002) 37–50.

[5] D.E. Bouchard, Optimal distribution feeder routing and optimal substation siz-ing and placement using evolutionary strategies, Electr. Comput. Eng. Conf.Proc. 2 (1994) 661–664.

[6] P.C. Paiva, H.M. Khodr, J.A. Domínguez-Navarro, J.M. Yusta, A.J. Urdaneta, Integralplanning of primary–secondary distribution systems using mixed integer linearprogramming, IEEE Trans. Power Syst. 20 (2005) 1134–1143.

[7] Igancio J. Ramirez-Rosado, J.A. Dominguez-Navarro, Possibilistic model basedon fuzzy sets for the multiobjective optimal planning of electric power distri-bution networks, IEEE Trans. Power Syst. 19 (2004) 1801–1810.

[8] Nicholas G. Boulaxis, Optimal feeder routing in distribution system planningusing dynamic programming technique and GIS facilities, IEEE Trans. PowerDeliv. 17 (2002) 242–248.

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17] S. Toune, H. Fudo, T. Genji, Y. Fukuyama, Y. Nakanishi, Comparative study ofmodern heuristic algorithms to service restoration in distribution systems, IEEETrans. Power Deliv. 17 (2002) 173–181.

18] M.R. Haghifam, Optimal allocation of tie points in radial distribution systemsusing a genetic algorithm, Eur. Trans. Electric Power 14 (2004) 85–96.

19] W. Wangdee, Bulk electric system reliability simulation and application, PhDThesis dissertation, University of Saskatchewan, 2005.

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Mehrdad Setayesh Nazar received the B.S. degree from Iran University of Scienceand Technology and M.S. degree in Electrical Engineering from Tarbiat ModaresUniversity, Tehran, Iran, in 1994 and 1996, respectively. He received Ph.D. degreein Electrical Engineering from Tarbiat Modares University in 2001. At present heis working toward post doctoral degree. His research interests include power sys-tems restructuring and distribution system planning. He is a member of IEEEand IET. He passed several short courses with Kema International Consultants,DSI and Shir International Consultants about power system restructuring and dis-tribution system planning Currently he is with Power and Water University ofTechnology.

M.S. degree in Electrical Engineering from Tehran University, Iran, in 1986 and 1990,respectively. He received Ph.D. degree in Electrical Engineering from Tarbiat ModaresUniversity (TMU), Tehran, Iran, in 1995. At present he is a full professor at TMU.His research interests include power systems restructuring and distribution systemplanning. He is a senior member of IEEE.

multiobjective electric distribution system expansion planning using hybrid energy hub concept

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