2014 Sept. Determine OC of DG Using Restore 06762945

8/10/2019 2014 Sept. Determine OC of DG Using Restore 06762945

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IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 5, SEPTEMBER 2014 2001

Determining the Optimal Capacity of RenewableDistributed Generation Using Restoration Methods

Sung-Yul Kim, Student Member, IEEE, Wook-Won Kim, and Jin-O. Kim, Senior Member, IEEE

AbstractThis paper proposes a methodology to determine theoptimal capacity of renewable distributed generation (RDG). Theobjective in this paper is to maximize cost-savings of energy notsupplied (ENS) as well as cost-savings of energy loss according

to RDG installation in a distribution network. Additionally, inorder to achieve this multi-objective optimization problem, both arestoration matrix and restoration availability are newly proposed.In this case study, a practical Korea Electric Power Corporation(KEPCO) system is presented and discussed, demonstrating the

performance and effectiveness of the proposed methods.

Index TermsEnergy loss, energy not supplied, optimalinstalled capacity, optimal interconnection bus, renewable dis-tributed generation, restoration availability, restoration matrix.

NOMENCLATURE

A. Abbreviations

CIC Customer interruption cost.

DG Distributed generation.

ENS Energy not supplied.

ESS Energy storage systems.

EUE Expected unserved energy.

IPP Independent power plant.

ISO Independent system operator.

KEPCO Korea Electric Power Corporation.

KMA Korea Meteorological Administration.

NPV Net present value.

O&M Operation and maintenance.

PF Power factor.

PV Photovoltaic.

RDG Renewable distributed generation.

RES Renewable energy sources.

Manuscript received July 30, 2012; revised January 13, 2013, April 27, 2013,July 11, 2013, and November 17, 2013; accepted January 13, 2014. Date ofpublication March 11, 2014; date of current version August 15, 2014. Paper no.TPWRS-00889-2012.

S.-Y. Kim is with the Department of Energy Engineering, Keimyung Univer-sity, Daegu, South Korea (e-mail: [email protected]).

W.-W. Kimand J.-O. Kim are with the Department of ElectricalEngineering,Hanyang University, Seoul, South Korea (e-mail: [email protected];[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TPWRS.2014.2305757

RPS Renewable portfolio standard.

WT Wind turbine.

B. Variables

Coefficients of performance of WT .

Power factor of WT .

ct Customer type.

Fault number in a contingency list.

Number of RDG.Number of bus of customers.

Number of bus interconnected with RDG.

Repair time of a fault .

Resistance of th element of Zbus matrix.

Location of RDG installation.

Observation time.

th hour in a day.

Interruption cost of each customer typeduring repair time .

Cost of ENS before RDG installation.

Cost of ENS for customers at bus afterRDG installation.

Total cost of ENS after RDG installation.

Cost of energy loss before RDG installation.

Total cost of energy loss after RDG

installation.

Net price for electricity.

Totalcost of ENS and energy loss before

RDG installation.

Optimal cost-saving of ENS and energy lossafter RDG installation.

Total cost of ENS and energy loss after RDG

installation.

Irradiation on location at observation time.

Total number of bus in a network.

Observation period (days).

Total number of , and periods make aday.

0885-8950 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.


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2002 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 29, NO. 5, SEPTEMBER 2014

Active power injection in bus at time .

Total active load of customers in bus attime .

Active load of customer type ct in bus at

time .

Peak load of customer type ct at bus .Customer load pattern of customer type ct

at time .

Active power of RDG at time , whichis installed in location and interconnectedwith bus .

Installed capacity of RDG , which isinstalled in location and interconnected

with bus .

Maximum capacity of RDG in a network.

Maximum capacity of RDG in bus .

Output probability of RDG at , whichis installed in location and interconnected

with bus .

Observed active power of PV atobservation time , which is installed inlocation and interconnected with bus .

Observed active power of WT in

observation time , which is installed inlocation and interconnected with bus .

Reactive power injection at bus at time .

Total reactive load of customers at bus attime .

Reactive power of RDG in time , whichis installed in location and interconnectedwith bus .

Observed reactive power of WT atobservation time , which is installed inlocation and interconnected with bus .

Restoration availability for a single RDG.

Restoration availability for multiple RDG.

Restoration matrix.

U Unit function.

Complex voltage at bus at time .

Cut-in speed of WT.

Cut-out speed of WT.

Rated wind speed of WT.

Wind speed on location at observationtime .

Inflection point for the constant output of

PV .

Amplitude of branch admittance between

bus and .

Corresponding efficiency of PV .

Failure rate of a fault .

Failure rate of customers at bus after RDG

installation.

Angle of branch admittance between bus

and .

C. Sets

Set of RDG .

Set of buses of customers .

Set of interconnection buses for RDG.

Set of installed locations for RDG.

I. INTRODUCTION

THE technical improvements and the desire of customers

for reliable and eco-friendly electric power have led to

increased interest in distributed generation (DG) including re-

newable energy sources (RES). Current energy policies of gov-

ernments also help to promote installation and operation of this

new type of generation through a feed-in tariff, quota system,

carbon tax and trading, and tax relief. As a result, clean en-

ergy technologies have become cost-competitive with conven-

tional power systems, and in the near future, the generation cost

of RES is expected to approach grid parity, which is the point

at which alternative means of generating electricity produces

power at a cost that is equal to or less than the price of pur-

chasing power from the grid [1][4]. Nevertheless, at present

the generation cost of RES is still more expensive than the gen-eration cost of conventional plants.Therefore, renewable energy

projects are still suffering from financial hardship due to renew-

able portfolio standard (RPS), which mandates that utilities and

other load serving entities must procure a significant portion of

their customers electricity needs from RES [5], [6]. In this sit-

uation, the planning of renewable distributed generation (RDG)

must be carefully considered towards achieving sustainable hy-

brid electric power systems.

Various methodologies to facilitate prospective investors to

arrive at an optimal planning for investment in RDG have been

proposed [7][16]. This research allows evaluation and com-

parison of total costs necessary in implementing the planningof RDG, considering construction costs, interconnection costs,

operation and maintenance (O&M) costs, and generation rev-

enue. Recently, an increasing concern for climate change has

focused the interest of the public towards environmental pro-

tection, thus [10], [11], and [15] presented an planning for in-

vestment in RDG considering carbon emission. In [12][16], the

net present value (NPV) analysis has been used for investment

analysis, and the planning with the highest NPV was selected.

The purpose of this research is to find the best choice of new

resources to be planned to maximize expected profits, from the

perspective of independent power plant (IPP).

On the other hand, there are many approaches for the plan-ning of RDG to minimize power loss and energy loss [17][22].


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KIMet al.: DETERMINING THE OPTIMAL CAPACITY OF RENEWABLE DISTRIBUTED GENERATION USING RESTORATION METHODS 2003

Hung et al. [17] presented analytical expressions forfinding size

and location of DG. In this work, the objective was power loss

minimization. Ochoaet al.[18] proposed the technique for ac-

commodating RDG to minimize not power losses, but energy

losses. Atwaet al.[19] proposed a planning technique for allo-

cating various types of DG to minimize annual energy loss. The

positive effects of RDG in these approaches are evaluated under

normal network conditions without any faults in a network. As

we consider an islanding condition in which RDG can continue

to power a location independently even though electrical grid

power is no longer present, customers load (that would oth-

erwise have been left disconnected until repair had been com-

pleted) can be transferred onto another part of the system such

as RDG [23]. Therefore, this operational condition can have a

remarkable effect on the distribution reliability such as energy

not supplied (ENS). Above all previous approaches, however,

overlook this positive effect of RDG when a fault occurs in a

network.

Dugan et al. [24] introduced the planning process for DG

considering the value of expected unserved energy (EUE) aswell as capacity constraints. Furthermore, [25] and [26] pro-

posed the multi-objective optimization method for the planning

of DG considering both ENS and energy loss. These previous

works reflected the positive effect of DG on reliability improve-

ment. In the event of a system failure, however, it is hard to

evaluate availability of electrical energy supplied from RDG to

interrupted customers. Because network configuration, priority

in power supply from RDG to customers, and the balance be-

tween load and generation from RDG should be considered in

order to evaluate this availability of RDG. Therefore, in this

paper, restoration methods (restoration matrix and restoration

availability) are newly proposed in order to solve this complexissue.

This paper tries to improve on earlier works by using ENS in

combination with energy loss according to the planning of RDG

using the proposed restoration methods from the perspective of

independent system operator (ISO). Here, the optimal installed

capacity and the optimal bus interconnected with RDG is deter-

mined by this proposed method. The proposed method for the

planning of RDG permits a better simulation.

The remainder of this paper is organized as follows:

Section II presents the output modeling of RDG. The proposed

restoration matrix and restoration availability are introduced

in Section III, and the purpose of these restoration methods is

to evaluate the reliability of a distribution network with RDG.

Section IV describes the methodology for determining the

optimal installed capacity and the optimal bus interconnected

with RDG. Section V presents numerical results of application

of the proposed methods in a practical KEPCO system, and

relevant observations are also addressed in this section. Finally,

conclusions and contributions to this paper are summarized in

Section VI.

II. RENEWABLEDISTRIBUTEDGENERATION

In general, DG is not centrally planned by the utility and not

dispatched. The installed capacity of DG is normally smallerthan 20 MW, and it is usually connected to a distribution system.

DG can be classified according to various criteria, which are the

purpose and the operation strategy, terminal characteristics, and

the output controllability of DG [17], [27], [28]. Based on the

output controllability of DG, coal-based generations and energy

storage systems (ESS) are classified into the controllable unit

type. On the other hand, the output of photovoltaic generation

(PV) and wind turbines (WT) is intermittent and uncontrollable

because these sources are affected by external factors such as

location and weather. In this paper, PV and WT are considered

for the output model of RDG.

A. Output of Renewable Distributed Generation

Based on the terminal characteristics, PV is regarded as the

unit supplying only active power to the grid, and WT is re-

garded as the unit supplying active power and consuming re-

active power.

1) Only Active Power Supply: PV: Active power of PV is

usually affected by irradiation, . The active power of PV

in observation time can be represented as follows [29]:

(1)

where PV is installed in location , and it is interconnected

with bus . In the relationship between irradiation and the output

of PV, after a certain irradiation point, further increases in irra-

diation produce relatively small changes in efficiency. This in-

flection point is represented as .

2) Active Power Supply and Reactive Power Consumption:

WT: The output extracted by the rotor blades of a WT is the dif-

ference between the upstream and the downstream wind powers.

It varies with the density of the air sweeping the blades and with

the cube of the wind speed [30]. For practical purposes, how-

ever, active power of WT is commonly described as shown in

(2) at the bottom of the page [31].

WT cannot export active power when the wind speed

is below the cut-in speed , and it is shut down for safety

reasons if the wind speed is higher than the cut-out speed .

The output of WT is proportional to the velocity between the

(2)


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cut-in speed and the rated wind speed . After this rated wind

speed, the output of WT remains constant.

Reactive power consumption of WT is represented as

follows:

(3)

where is the power factor (PF) of WT .

B. Output Model of Renewable Distributed Generation

The output data of RDG is analyzed by using the output pa-

rameters of RDG and the weather conditions during observation

period, . Based on these data, the output model of RDG in

time can be determined by using a linear rounding method,

and the output probability of RDG is defined by the per-unit

value as follows [32]:

(4)

(5)

where is one among 24 hours in a day.

Finally, the output of RDG in time can be represented in

the following equations, where RDG is installed in location

and interconnected with bus :

(6)

(7)

In here, is zero while the type of RDG is PV.

III. RESTORATION VIA RENEWABLE

DISTRIBUTEDGENERATION

An islanding condition can have a remarkable effect on the

reliability in a network with RDG. To evaluate this effect, there

are a few issues that need to be considered including: 1) net-

work confi

guration; 2) priority in power supply from RDG tocustomers; 3) the balance between load and generation from

RDG. However, when a fault occurs in a network, it is hard to

evaluate availability of electrical energy supplied from RDG to

interrupted customers while simultaneously considering these

issues. Furthermore, this priority in power supply differs from

the configuration and the fault in a network, and also this bal-

ance between load and generation depends on the network con-

figuration, the fault and time. In order to solve these compli-

cated problems, restoration matrix and restoration availability

are newly proposed in this paper. These restoration methods are

innovative techniques for evaluating the distribution reliability

compared to the method introduced in [5]. In this paper, these

restoration methods are used to evaluate ENS for the optimal

planning of RDG.

Fig. 1. Simple distribution network with RDG.

A. Restoration Matrix

Let RDG be interconnected with bus and a fault occur in a

network. Then, , called the restoration matrix, offers

valuable structural information about priority in power supply

from RDG to customers at bus , and it is formulated as follows:

if RDG cannot supply power to customers

if customers are not affected by the fault

priority.

(8)

Regardless of a fault in a network, if customers at bus

can be supplied with power from the main grid, is

zero. Additionally, zero also has the meaning of no cus-

tomers at bus . On the other hand, if customers at bus cannot

be supplied with power from the main grid or RDG due to net-

work configuration during a fault , then is .For the rest, the value of the restoration matrix indicates a nat-

ural number as priority in power supply from RDG to customers

at bus .

Fig. 1 depicts a simple case in order to explain restoration

matrix. In here, RDG is interconnected with bus 6 ,

and customer and are located in bus 3, 5 and 7, re-

spectively. When all 6 line faults are postulated in this network,

restoration matrix can be determined in (9) by using definition

(8):

where row and column in restoration matrix are indicated by

and , respectively.

When a fault occurs on line 1 , RDG interconnected with

bus 6 should consider to be a top priority (denoted by 1)

in order to supply electricity. is second on the priority list

(denoted by 2), followed by (denoted by 3) in this case


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. Additionally, when a fault

occurs on line 3 can be supplied with power from the

main grid (denoted by 0), regardless of the output of RDG as

well as the fault on line 3. , on the other hand, cannot be

supplied with power from the main grid or RDG (denoted by

) due to this network configuration in Fig. 1

.

B. Restoration Availability

Using the restoration matrix, priority in the power supply is

determined according to the faults in a network and buses in-

terconnected with RDG. However, when a customer has the

highest priority from power supply, it does not automatically

mean the customer will be supplied with power during a fault in

a network. This is because the balance between load and gener-

ation is the basic rule of all power systems for a stable energy

supply. Therefore, in order to evaluate availability of electrical

energy supplied from RDG to interrupted customers, not only

electricity supply priorities by restoration matrix, but the rela-tionship between load and generations in time is required.

In this respect, restoration availability is proposed in addition to

restoration matrix.

, called restoration availability, offers valuable

information about whether this power system maintains the bal-

ance between customers load at bus and the output of RDG

in time when a fault occurs in a network. Here, the output

of RDG can be affected by the location for RDG installation,

because weather conditions differ from location .

In a network with a single RDG, restoration availability is

determined as follows:

(10)

(11)

(12)

(13)

(14)

0 means restoration via RDG is not available. 1, on the

contrary, means restoration via RDG is available. Here, ct is a

customer type such as public, commercial, industrial and resi-

dential. is the set of bus with customers whose electrical

load should be supplied from RDG during a fault . Active

load at bus in time is , which is evaluated by using

the load pattern of each customer type and

the peak load of each customer type at bus . Here,

is expressed as the per-unit value based on peak

load of each customer type. As shown in (12), basically, restora-

tion availability of RDG is determined by the balance between

customers load and the output of RDG in time . Here, U is

the unit function.

Based on (10)(14), when various RDG are installed in a net-

work, restoration availability is determined as follows:

(15)

where is the set of RDG . and are the set of installed

locations for RDG and the set of interconnection bus for

RDG, respectively.

Restoration methods proposed in this paper are applied to

evaluate the sum of cost-saving of ENS according to installed

capacity and the bus interconnected with RDG in Section IV.

IV. PROBLEMFORMULATION

In this section, the method for determining the optimal in-

stalled capacity and interconnected bus with RDG is proposed.

In here, there are two different objectives. The first objective

is to minimize ENS when a fault occurs in a network (under

abnormal network conditions), and the second one is to mini-

mize energy loss without any fault (under normal network con-

ditions). In this paper, these two objectives combined into a

multi-objective optimization problem in order to evaluate all ef-

fects of RDG installation. The purpose of this multi-objective

optimization problem is to maximize the sum of cost-saving of

ENS and cost-saving of energy loss, according to installed ca-

pacity and the bus interconnected with RDG.

The multi-objective function in this paper can be formulated

as follows:

(16)

(17)

(18)

where is t he sum o f cost o f ENS and cost o f energy

loss , before new RDG is installed in a network (denoted

by Base Case). and are ENS cost and energy

loss cost after RDG is installed in a network, respectively. The

values of and are evaluated by considering all

possible factors, which are all feasible RDG (in the set ),

permitted locations (in the set ) and interconnectable buses

(in the set ).The remainder of this section is composed of four parts. The

first and second part represent the way to evaluate

using the proposed restoration methods and ac-

cording to the planning of RDG, respectively. The third part

describes constraints for this multi-objective optimization,

and the last part depicts the flow chart of the proposed plan-

ning technique.

A. Cost of Energy Not Supplied:

Customers are composed of various types, and customer in-

terruption costs (CIC [$/kWh]) are different in each customer

type and repair time. In this paper, CIC of customers at bus is

evaluated by using CIC of each customer type and


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Fig. 3. Cases system: Icheon and Jangheung in South Korea.

As a result, energy loss costs in the multi-objective optimiza-

tion problem are determined as follows:

(28)

where is the net price for electricity, and this equation

indicates the sum of energy loss cost for one year because ENS

is generally evaluated on a yearly basis.

C. Constraints

The multi-objective optimization problem should be mini-

mized subject to certain equality and inequality constraints.

Distribution powerflow equations

(29)

(30)

In these separating the active and reactive parts, and

are the amplitude and angle of branch admittance between bus

and bus [35].

Inequality constraints relate to keeping the magnitude of cur-

rents at all lines and the magnitude of voltages at all buses within

the permissible limits.

Limits on line current magnitude

(31)

Limits on bus voltage magnitude

(32)

Limits on RDG capacity of interconnected bus

(33)

Limits on RDG capacity of a distribution network

(34)

where is maximum-allowable capacity of RDG in

bus . For example, according to the South Korean grid code,

maximum-allowable capacity of RDG was restricted to 20 MW

in a distribution network. is maximum-allowableRDG capacity in a distribution network.

D. Flow Chart

The process for determining the optimal capacity of RDG is

described as shown in Fig. 2.

V. CASESTUDY

In order to demonstrate the validity of the solution sets

obtained by the proposed methods, a 154/22.9-kV distribution

system is adopted as a case system, and this case system is a

practical KEPCO (Korea Electric Power Corporation) system

in the area of Icheon and Jangheung in South Korea.It is assumed that PV and WT, shown in Fig. 3, are installed

in the adjacent area of this case system. According to the South

Korean grid code for interconnection of RDG in a distribution

network, maximum-allowable capacity of RDG in each inter-

connection bus is as shown in Table I.

Base Case shows the original network prior to the installation

of RDG such as PV and WT. In Case 1, one of PV and WT is

installed in the case system, and in Case 2, both of them are

installed simultaneously. All system data and grid constraints

are applied with KEPCO.

Weather is still very stochastic and prone to uncertainty in

terms of forecasts, even if various research studies have been

performed [33], [36]. In this paper, the output of RDG at time

is evaluated by using historical measurement data. The time-


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TABLE IMAX-ALLOWABLECAPACITY OFRDG IN EACHBUS

TABLE II

CUSTOMERINTERRUPTION COST OFEACH CUSTOMERTYPE

TABLE IIICUSTOMERSPEAKLOAD

based output models for PV and WT are derived from irradia-

tion data and wind velocity data, respectively. These weather

data have been acquired by KMA (Korea Meteorological Ad-

ministration) during 2010, and depicted in Fig. 9 (Appendix).

In this case study, an islanding operation of RDG is allowed

in order to maximize the benefits from RDG, because this oper-

ation mode can improve the distribution reliability.

The failure of lines, buses and transformers can be included

in a contingency list. In this paper, the failure of all lines is pos-tulated for contingency, and it is assumed that these lines have a

failure rateof 0.1 f/kmyr.Repair timein the mainfeeder ( )

is 4 hours, and repair time in the lateral distributors ( ) is

1 hour.

Customer type (ct) is divided into public, commercial, indus-

trial and residential in this paper. CIC ( depends on

customer type and interruption time. CIC of each customer type,

shown in Table II, is bifurcated into 1 hour and 4 hours as inter-

ruption time [37][40].

All customer load patterns in this case study are based on the

investigation from 2003 to 2007 in South Korea. Customers

peak load and load patterns are shown in Table III and Fig. 10,

respectively. Using these data, and (13) and (14), hourly power

demand of each customer is postulated.

Energy loss is evaluated by the relation between customers

load and the output of RDG in time as well as the grid data

in the case system.

Unit installed capacity of RDG is 1 kW for implementation

of iterative technique, and the results in this case study are sim-

ulated and evaluated by Matlab 7.0.4. All computer programs

were run on a Windows 7-based PC (Intel Core i7 Q740 pro-

cessor and 12 GB RAM).

A. Analysis on Cost of ENS

In Base Case where there are no RDG in the case system, cost

of ENS in each customer is evaluated as shown in Table IV.

TABLE IVBASE CASE: COST OFENS

TABLE V

CASE 1: THEPLANNING OFRDG TO REDUCECOST OFENS

TABLE VICASE 2: THEPLANNING OFRDG TO REDUCECOST OFENS

In order to evaluate ENS when RDG is installed and inter-

connected to the case system, restoration matrix according to

interconnected bus with RDG should be determined first. Fig. 8

shows restoration matrix in this case study, which is the part of

Matlab source code.

In Case 1 where one of PV and WT is installed, lower cost

of ENS compared to the results in Base Case are analyzed as

shown in Table V.

These results in Table V show that RDG installation doesnot always improve ENS. All buses interconnected with RDG

but bus 2, 4 and 6 appear to have no effect on ENS, because

ENS is affected by structural characteristics of a network (de-

fined as restoration matrix) and the balance between customers

load and the output of RDG in time (defined as restoration

availability).

In case of only PV installation, the most effective way to im-

prove ENS is to install PV with 2974 kW (interconnection bus

. In case of only WT installation, the most effective way

to improve ENS is to install WT with 2824 kW (interconnection

bus ).

RDG is generally interconnected to the nearest bus for gener-ation expansion planning. In this case system, bus 4 for PV and

bus 3 for WT are the nearest buses. However, when PV is in-

terconnected to bus 6, this planning can reduce the cost of ENS

more than 1.8% compared to the conventional planning where

PV is interconnected to bus 4. Furthermore, to interconnect WT

with the nearest bus 3 has no effect on ENS.

In Case 2 where both of PV and WT are installed, various

plans with two RDG can reduce costs of ENS compared to the

results in Base Case. Table VI shows the top 3 plans among all

feasible plans in Case 2.

Although both of PV and WT are installed simultaneously in

Case 2, all plans are not always able to generate cost-savings

for ENS. Therefore, it is important to determine which bus we

choose to interconnect, as well as the installed RDG capacity.


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TABLE VIITOP8 PLANS OFRDG TO IMPROVE COST-SAVING OFENS

TABLE VIIICASE 1: THEPLANNING OFRDG TO REDUCE ENERGY LOSS

Considering all cases, the top 8 plans of RDG are arranged in

order of improved cost-saving of ENS as shown in Table VII.

With respect to improved cost-savings of ENS due to the

planning of RDG, the most effective way is to install PV with2974 kW (interconnection bus ) and WT with 2824 kW

(interconnection bus ) simultaneously. Here, costs of ENS

can be reduced by 4.39% compared to the results in Base Case,

where there is no RDG in the case system. Additionally, this

proposed planning can reduce the cost of ENS more than 2.8%

compared to the conventional planning where RDG is intercon-

nected to the nearest bus in a network.

B. Analysis on Energy Loss Cost

In Base Case, energy loss and energy loss cost are evalu-

ated as 7830.6889 MWh and $783 068.9, respectively, by power

flow.In Case 1, Table VIII shows the optimal capacity of RDG

according to each bus , from a viewpoint of energy loss.

In Table VIII, bus 1 for RDG interconnection does not influ-

ence energy loss at all, because bus 1 is the secondary part of

the main grid.

From the results in Base Case and Table VIII, energy loss and

cost-saving of energy loss in Case 1, can be depicted in Figs. 4

and 5.

With respect to improved cost-saving of energy loss due to

the planning of RDG, the most effective way is to interconnect

RDG with bus 5. Here, two factors lead to this result. At first,

the electrical distance between and themain grid is relatively

far. Secondly, customers load in is the largest in this case

system as shown in Table III. When PV is interconnected with

Fig. 4. Case 1(PV): energy loss and cost-saving of energy loss.

Fig. 5. Case 1(WT): energy loss and cost-saving of energy loss.

bus 5, this planning can reduce energy loss more than 1.5% com-

pared to the conventional planning where PV is interconnected

with the nearest bus 4. Additionally, when WT is interconnected

with bus 5, this planning also can reduce energy loss more than

8.8% compared to the conventional planning where WT is in-

terconnected with the nearest bus 3. Therefore, the planning of

RDG which is interconnected with bus 5, is most effective to

improve energy loss.

In Case 2, Table IX shows the optimal capacity of RDG and

interconnection bus , from a viewpoint of energy loss.

Considering all Cases, the top 10 plans of RDG are arranged

in order of improved cost-saving of energy loss as shown in

Table X.

With respect to improved cost-saving of energy loss due to

the planning of RDG, the most effective way is to install PV

with 3000 kW (interconnection bus ) and WT with 3000

kW (interconnection bus ) simultaneously. Here, cost of

energy loss can be reduced by 40.7% compared to the results in

Base Case, where there is no RDG in the case system.

C. Optimal Planning of RDG

The results in Case 1 are depicted in Figs. 6 and 7. As consid-

ering only cost-saving of energy loss, the most effective way is


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TABLE IXCASE 2: THEPLANNING OFRDG TO REDUCEENERGY LOSS

TABLE XTOP10 PLANS OFRDG TO IMPROVE COST-SAVING OFENERGY LOSS

Fig. 6. Case 1(PV): total improved cost-savings.

to interconnect RDG with bus 5. However, this approach over-

looks the effects of RDG when a fault occurs in a network.

Therefore, the optimal planning of RDG proposed in this paper

is determined by considering both sides of ENS and energy loss.

As a result, the optimal planning of PV in Case 1 is to install PV

with 3000 kW, and to interconnect PV with bus 6. On the other

hand, the optimal planning of WT in Case1 is to install WT with

3000 kW, and to interconnect WT with bus 4. By the proposed

Fig. 7. Case 1(WT): total improved cost-savings.

TABLE XIPROPOSED TOP10 PLANS OFRDG

method in this paper, the planning of PV can reduce annual cost

of ENS and energy loss, $487 074.9, and the planning of WT

can reduce them, $338 675.6.

Finally, the top 10 plans of RDG are arranged in order of total

improved cost-savings as shown in Table XI.

The optimal planning of RDG in this case system is to install

PV with 3000 kW (interconnection bus ) and WT with

3000 kW (interconnection bus ) simultaneously. Further-

more, from this optimal planning, costs of ENS can be reduced

by 4.39% and energy loss costs can be reduced by 39.12% com-pared to without RDG in this case system. This optimal planning

of RDG is expected to achieve significant annual cost-savings of

$694 602. From the ISOs viewpoint, it is the maximum benefit

via RDG, considering under normal (energy loss) and abnormal

conditions (ENS) using the proposed restoration methods.

VI. CONCLUSION

In this paper, a methodology to determine the optimal bus

interconnected with RDG, as well as the optimal installed ca-

pacity of RDG is proposed. Both of the ENS and energy loss

are considered to solve this multi-objective problem. In here,

restoration methods are also proposed to evaluate the effect on

ENS according to the planning of RDG.


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Fig. 8. Restoration matrix.

Fig. 9. Weather data. (a) Irradiation in . (b) Wind speed in .

In order to verify the proposed methods, data sets from real

power systems in Icheon and Jangheung, South Korea are ap-

plied to the case study. The results in the case study confirm that

the proposed methods are more suitable to determine the op-

timal planning of RDG compared to the conventional methods,

because cost-savings of ENS under abnormal conditions as well

as cost-savings of energy loss under normal conditions due to

RDG installation are evaluated simultaneously.

The planning of RDG in this paper is approached from a

viewpoint of ISO, and this paper is innovative and fundamental

towards sustainability and increasing the economic attractive-

ness of RDG. In this sense, it can provide valuable insights to

stakeholders in governments and companies.

In this paper, the proposed restoration methods are only due

to vicinity to RDG. In our future work, as considering the other

factors in addition to vicinity, further developed methods will be

proposed to improve its economic impacts and effectiveness.

APPENDIX

Figs. 810 show the restoration matrix, weather data, and load

patterns by customer type, respectively.


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Fig. 10. Load patterns by customer type. (a) Public. (b) Commercial. (c) Industry. (d) Residential.

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Sung-Yul Kim (S11) received the B.S. and Ph.D. degrees in electrical en-gineering from Hanyang University, Seoul, South Korea, in 2007 and 2012,respectively.

From 2012 to 2013, he was a research assistant at Georgia Institute of Tech-nology, Atlanta, GA, USA. Since 2013, he has been with the Department ofEnergy Engineering, Keimyung University, Daegu, South Korea. His main re-search interests include computer aided optimization, renewable energy sources

applied to smart grid, and power system reliability.

Wook-Won Kimreceived the B.S. and M.S. degrees in electrical engineeringfrom Hanyang University, Seoul, South Korea, in 2008 and 2011, respectively.He is pursuing thePh.D.degree in electricalengineeringof Hanyang University.

His research interests include power system reliability, optimal schedulingof energy storage system with heuristic algorithm, and forecasting renewableenergy sources.

Jin-O. Kim (SM03) received the B.S. and M.S. degrees in electrical engi-neering from Seoul National University, Seoul, South Korea, and the Ph.D. de-gree from Texas A&M University, College Station, TX, USA.

He is presently a Professor with the Department of Electrical Engineering,Hanyang University, Seoul, South Korea. His research interests include powersystem reliability, planning, and power economics applied to smart grid.

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