[ieee 2010 4th international power engineering and optimization conference (peoco) - shah alam,...

The 4th International Power Engineering and Optimization Conf. (PEOCO2010), Shah Alam, Selangor, MALAYSIA: 23-24 June 2010

Multi Objective Placement of Distributed Generation

Phan Thi Thanh Binh, Nguyen Huu Quoc, Phan Quoc Dung, Le Dinh Khoa Faculty of Electrical & Electronic Engineering

University of Technology Ho Chi Minh City, Viet Nam

Abstract—The distributed generation (DG) interconnection into the distribution network may lead to significant changes in system. The installation of DG must be met network requirement such as: breaking capacity of switchgears, decreasing loss, increasing reliability and voltage quality. Beside, in this work, the paper also considers the objective function concerning the utility and DG private owner benefits. The resolving is based on fuzzy set and genetic algorithm (GA). These objectives are chosen more suitable for Vietnamese conditions. The DG with unavailable energy source such as solar and wind are considered. The paper also presented the general case when many types of DG can be involved.

I. INTRODUCTION The distributed generation (DG) interconnection into the power network may lead to significant changes in system. Some impacts of DG are:

• Increase in short circuit current, so it may exceed the capacity of circuit breaker.

• Deterioration of sensitivity to faults: depending of the location of fault, the sensitivity of relay system is liable to deteriorate.

• DG can cause the back-flow from DG and therefore, the voltage rise in network.

• DG in some cases can deteriorate the stability (especially the voltage stability) in network.

• If the penetration of DG is large, in some faults, the reliability of distribution of system can be worsened than the case without DG. The installation of DG at non-optimal places can result

in an increase in system losses, implying in an increase in cost and causing an effect opposite to the desired.

For specified goal in DG installing, there are many papers. Some author concentrated only on power loss, other on reliability, on short circuit level [3], on stability enhancement [9]. Some papers present a methodology for optimal DG allocation and sizing in order to minimize the losses and to guarantee acceptable reliability level and voltage profile [2]. The reliability, the voltage profile became the constraints. The objective function is based on losses.

Another considered installing DG as the multi objective problem: in [4] proposed two objective functions: energy loss, voltage profile; in [6] used the goal with energy loses and ∆U; in [7] the cost for installing DG, power loss and reliability indexes.

For reliability estimation, all paper ignored the availability of resource such as in [2], [8], and [7] supposed that all DG energy source is considered always available (that means DG based on gas and diesel technology-which are being widely used for DG units).

For resolving this multi-objective programming, some papers based on linear programming method [1], another based on genetic algorithm [2] [3] transforming multi-objective into one optimization with one objective. Some author as in [5] used the adaptive weight particle swarm optimization. The GA is also preferable method [7]. In [6] used fuzzy goal programming with GA with energy loses and ∆U. But in those papers the monotype DG is considered.

In this work, we consider the DG with unavailable energy source such as solar and wind. The unavailability duration is considered as only the time after fault clearing and re-connecting the DG. The paper also presented the general case when many types of DG can be involved. Some constraints can be treated as severed, but some can be regarded as not strictly with some violation, such as breaking capacity of switchgear. It depends on the utility. So the violation of these constraints can be expressed as fuzzy with membership function. The multi-objective problem will be solved on the GA and fuzzy logic considering also the different weights of objective function.

II. MODELING

1) The power loss is the big problem for many utilities. The purpose to maximal energy loss reducing in comparison with the case non DG will be carried out by the combination of DG location and their sizing is:

Max { }ilossloss

iloss AAA −=Δ 0 (1)

Where: A 0loss : the energy loss without DG.

A iloss : the energy loss in system with DG existing

for i – DG combination. One combination of DG is defined as the

combination of DG (types, number of units) at all buses in the network.

The power flow program will be carried out for power loss evaluation regarding DG as PV bus (for synchronous generator), PQ bus (wind or solar DG).

2) For voltage profile enhancement the following objective function is introduced:

978-1-4244-7128-7/10/$26.00 ©2010 IEEE 484


Min { }|)0.1max(|)max( i

jij UU −=Δ (2)

Where U ij : voltage (in per unit) at bus j with i-combination

of DG.

3) In order to guaranty the reliability of system, the paper calculates the power not supplied after the three phases short-circuit. In Vietnam, the more detail rate values for calculating the reliability are not yet available. So we just consider the time after fault clearing assuming that all DG are in ready state. The momentary interruption due to DG ready state is neglected. For more comprehensive illustration, the further explanation is based on Fig.1

Fig.1 - Illustrative network.

• Without DG:

a) The three phases short circuit happened in lateral: if there is fuse or ACR (Recloser) on this lateral, the quantity of cut loads is depended on fault location. If fault is happened upstream of these devices, or especially where there is not no protective devices on lateral, the part of feeder downstream of nearest upstream recloser or CB on main feeder will be unloaded. b) The fault happened in main feeder, determine the closest recloser upstream, all the loads downstream of this recloser are cut.

• With DG: After tripping of corresponded protective devices there maybe several islanded parts with DG. So the consumers will be loaded at some level depending on availability of DG and the load level of consumption in those moments. For example fault is at bus 14; the islanded region from bus 15 to bus 18 will be feed by DG at bus 18 (if this DG is not wind DG). The clearing fault at bus 2 will create islanded region downstream of R 1. Varying the fault location, we can evaluate the

reliability of this network at every hour of day. For reliability estimation, the time depended load curve for demand side and supply side (DG) also will be considered.

And each DG has its own day-mean load curve availability. The DG based on diesel or gas has the flat

curve, the solar or wind DG has the curve changing during day time. If the typical load curves for each section are given, we can calculate the accumulative power not supplied in day due to fault.

The objective function has the following form:

Max (reliability) = Max (T

iT

SAIFISAIFISAIFI −

) (3)

Where: SAIFI T : the total accumulative power not supplied

when the fault happened in sequence in all the sections in the case of without DG.

SAIFI i : the total accumulative power not supplied when the fault happened in sequence in all the sections with the i-combination of DG.

4) For assuring the breaking capacity of switchgear in

system we proposed this problem as one flexible constraint depending on the individual utility. Some utility may do not allow this violation, but some utility have wiliness to change the equipment due to the existence of DG. So it will be expressed as crisp constraints (yes or no), or as fuzzy constraint.

Some switchgear can not be able replaced. For these ones we can propose in form of constraint.

In the form of objective, it has the following form: Min (Breaking) = Min (Equip i ) (4)

Where: Equip i - the number of switchgear that can be replaced due to the violation of breaking capacity with the i-combination of DG.

The three phase short-circuit is applied on every branch. Because of available voltage rise in the case of DG and the availability of resource, the short circuit currents for every hour can be calculated with the daily load curve. The purpose of this is to find out the violation of braking capacity of switchgear in the network.

5) Concerning the objective on installation capital

installation, we considered two cases: DG belonged to private owner and DG belonged to the utility.

For both cases, we propose the following objective function:

Min (time) = Min (T i =i

i

sell

cap

ZZ

) (5)

Where: T i : simple pay back period for i-combination of DG.

icapZ : the installation capital for i-combination of DG.

isellZ : owner (private or utility) benefit from selling total annual amount of produced electricity of DG.

So (5) is to find the minimum value of simple pay back period. For private owner, the weighted coefficient of (5) is higher than the case of utility owner. In the case of utility owner, (5) is one of the objective function as another ones. But for the private owner, the owner is one partner as the utility, so we propose the weighted coefficient of (5) of 0.4.

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We assumed that in each lateral, there will be only one type of DG and this DG will be located in one location. This is because the limited length of lateral. Those DG belonged to customer regarded as negative load and will be included in bus load so in future do not be considered in this paper.

The case where DG cannot be located at some buses is also considered.

The type of DG considered in Vietnam is solar or wind, diesel or gas (perhaps on the consumer site). Each type of DG will have several unit gammas.

Other constraints can be incorporated in such as: • The total capacity of DG can not be exceeded some

levels. • The load on lines is less than permissible value.

III. GA and FUZZY

Concerning with the multi objective function, we can express each function by fuzzy set. The membership function for each objective function is built as followed.

For the voltage enhancement, the following membership function is considered:

1μ (max( ijUΔ )) =

⎪⎪⎩

⎪⎪⎨

⎧

Δ≥Δ

ΔΔ≤ΔΔ−Δ

Δ−Δ−

ΔΔ

max

maxminminmax

min

min

)(max0

)max()max(

1

)max(1

UUif

UUUifUU

UUUUif

ij

ij

ij

ij

≺

≺(6)

Where: minUΔ : the standard deviation

maxUΔ : some permissible deviation. ijUΔ : voltage deviation at bus j due to i-DG

combination.

The membership functions for power loss objective:

⎪⎩

⎪⎨

⎧≤≤

−=

Δ=Δ

0

00

0

02

0

0)(

lossiloss

lossiloss

loss

ilossloss

loss

iloss

iloss

AAif

AAifA

AAAA

Aμ (7)

Concerning about the reliability, the membership

function may be expressed as:

T

iT

SAIFISAIFISAIFI

yreliabilit−

=)(3μ (8)

Dealing with the breaking capacity, the following

objective’s membership function is considered:

T

i

EquipEquip

breaking −= 1)(4μ (9)

Where Equip T : the total number of the set (denoted as set A)

that consists of the switchgears be able replaced. Equip i : the total number of switchgears belonged to

the set A that must be replaced due to the existence of DG.

The membership function for installation capital

installation objective:

minmax

max5 )(

TTTT

time i

−−

=μ (10)

Where:

maxT : the longest simple pay back period.

minT : the fastest simple pay back period. GA is suitable for discrete optimization. The paper

proposed the following fitness function:

Fitness func =1 - ∑=

5

1

))((k

ikkk Xfμω (11)

Where kω : weight coefficients. These coefficients are chosen by decision maker and less than 1.

One population has the form, for example as followed:

nCombinatioi

nBusBusBusBus

_

321

1101...011010000011

• Two of first bits will be coded for the sitting of DG:

+ 00: no DG. + 01: Wind DG. + 10: Solar DG. + 11: DG diesel.

• Two of end bits will be coded for the sizing of DG: + 00: level for capacity installed is 1. + 01: level 2. + 10: level 3. + 11: level 4.

The constraints must be verified for each population. Criteria for stopping:

• Generations. • Time limit. • Fitness limit. • Stall generations. • Stall time limit.

The proposed program is very convenient for users. The number of objective functions, constraints, configuration of networks can be updated.

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IV. NUMERICAL RESULTS

The following distribution network is examined:

Fig.2: The outline of distribution network.

The source S1 is an unlimited source. At each bus of the system, the fuse is installed to protect distribution transformer, fuse (F) is located at the beginning of lateral, recloser R is set in main feeder and some laterals. In this example, three type of DG will be considered: solar, wind and diesel (gas). The parameters of the line:

+ Main feeder: AC-240, permissible current: 494 A.

+ Lateral: AC-120, permissible current: 291 A.

TABLE 1: THE LENGTH OF LINE

Section 0-1 1-2 2-3 3-4 4-5 Length (km) 1.2 1 0.6 0.5 0.8

Section 5-6 6-7 7-8 8-9 9-10 Length (km) 0.7 0.8 0.9 0.8 0.7

Section 2-11 11-12 12-13 5-14 14-15 Length (km) 1 0.4 0.6 0.7 0.6



The switchgear installed in series on branch is

considered not to be replaced. The breaking capacity of switchgears is 25kA, except CB: 45kA.

Fuse that can be replaced at bus 1, 2, 3, 13 → 25. The breaking capacity of fuse at each bus is 25kA, except bus 1: 45kA, bus 2: 36 kA.

DGs have just the active power (cos(ϕ )=1). For each

type of DG, there are 4 level of capacity installed:

TABLE 2: CAPACITY OF DGs

Capacity Type

Level 1 (MW)

Level 2 (MW)

Level 3 (MW)

Level 4 (MW)

Wind 0.4 0.8 1.2 1.6 Solar 0.25 0.5 0.75 1 Diesel 0.4 0.8 1.2 1.6

TABLE 3: THE INSTALLATION CAPITAL

Bus Wind (USD /kW)

Solar (USD /kW)

Diesel (USD /kW)

Bus Wind (USD /kW)

Solar USD /kW

Diesel (USD /kW)

1 - - 520 14 680 - - 2 - - 520 15 700 - - 3 700 1100 500 16 720 - - 4 700 1200 500 17 690 - - 5 800 900 500 18 700 - - 6 700 1100 510 19 - - 530 7 750 1000 510 20 - - 550 8 750 1000 510 21 - - 540 9 760 1000 500 22 - - 540

10 770 1000 500 23 800 1100 500 11 - 900 - 24 700 1000 500 12 - 1000 - 25 750 1050 500 13 - 950 -

There are some requirements of DG type available at

some buses: for lateral 2: for solar resource only; wind DG can be located on lateral 5 only; diesel DG may be located on lateral 7.

Benefit from selling 1kWh (after excluding the operational costs): wind 0.06 USD, solar 0.06USD, diesel 0.02 USD.

The Weighted Coefficients: for installation capital installation objective: 0.4; for reliability objective: 0.3; for power loss objective: 0.2; for voltage objective: 0.05; for breaking capacity objective: 0.05.

The load curve of load at each bus and DG are displayed in Figure 3, 4 and 5. Availability of Diesel DG is always 100%.

Fig.3 - Availability of Wind DG for Dry Season – Rainy Season

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Fig.4 - Availability of Solar DG for Dry Season – Rainy Season.

Fig.5 - The typical daily load curve for one bus.

Result:

The results of examining the different objective functions is given in table 4.

TABLE 4: RESULTS FOR DIFFERENT OBJECTIVE FUNCTIONS

Location/

Type/ Capacity

UΔ μ 2 μ 3 μ 4

μ 5

For 5 objective functions

- Bus 9: diesel of 1.2 MW. - Bus 14: wind of

0.4 MW. - Bus 23: diesel of 0.4 MW.

0.0018 0.9411 0.1222 1 0.9281

For only one

reliability objective

- Bus 9: diesel of 1.6 MW. - Bus 23: diesel of 0.4 MW.

0.0015 0.9454 0.1411 1 0.8921

For only one loss objective


1.2 MW. - Bus 24: diesel of 0.4 MW.

0.0011 0.9664 0.0662 1 0.9265

For only one

voltage objective


0.8 MW. - Bus 22: diesel of 0.4 MW. - Bus 24: diesel of 0.4 MW.

0.0011 0.9611 0.0761 1 0.9094

For capital installation

- Bus 14: wind of

1.6 MW 0.0207 0.3075 0 1 1

For 5 objective functions, in comparison with the case

of non DG, the following indicators show the advantages of existence of DG:

• The supplied power increased about 12.22%. • The loss is decreased about 94.11%.

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V. CONCLUSION

The DG placement in distribution must meet some objective functions in order to enhance the quality of network. The proposed objectives of the paper are based on breaking capacity of switchgears, decreasing loss, increasing reliability, voltage quality and capital installation and they are met to Vietnamese conditions. These objective functions must reflex not only the benefit of utility but also the DG private owner. The simultaneous existing of different DG types, the availability of distributed resource is also introduced and that makes the solution more suitable. The multi-objective problems are solved on the GA and fuzzy logic considering also the different weights of objective function. Beside, the expanding of objective and constraints are available in this work, so the paper’s program is very convenient for users.

REFERENCES

[1] Andrew Kean et al, Optimal allocation of Embedded Generation on distribution networks, IEEE Trans. On power systems, vol.20, August 2005.

[2] Carmen L.T. Borges, Djalma M. Falcao, Optimal DG allocation for reliability, losses, and voltage improvement, Electrical Power and Energy Systems 28, 2006, 413-420rx.

[3] Celi G et al, Optimal DG allocation in MV distribution networks. Proceeding of IEEE PES conference on power industry computer applications-PICA 2001, Australia, 2001, p 81-86.

[4] Alexandre Barin et al, Analysis of the Impact of DG sources considering the appropriate choice of Parameters in a Multi-objective approach for distribution system planning, IEEE 2008.

[5] Witoon Promme et al, Optimal Multi-Distributed Generation Placement by adaptive weight Particle Swarm Optimization, International conference on control, Automation and systems 2008, Korea.

[6] Kyu-Ho Kim et al, Multiobjective distributed generation placement using fuzzy goal programming with genetic algorithm, European trans. on electrical Power 2008, 18, p 217-2390.

[7] Jen-Hao-Teng et al., Value-based distributed generator placements for service quality improvements, Electrical Power and Energy System 29 (2007), p. 268-274.

[8] D.H.Popovic, J.A. Greatbank, Placement of DG and recloser for distribution network security and reliability, Electrical Power and Energy System 27 (2005) 398-408.

[9] Hasn Heiadati et al, A method for placement of DG units in distribution networks, IEEE Trans. On power delivery, vol.23, July 2008, p.1620-1628.

Phan Thi Thanh Binh received her B.Eng degree in 1984 , received Ph.D degree in 1995 . She is an associate professor in the Power Delivery Engineering of Ho Chi Minh city University of Technology Viet Nam and lecturing in here from 1984. Her research interests include: Load Forecasting, State Estimation, Demand Side Management and Stability of Power system.

Nguyen Huu Quoc graduated from the University of Technology, Ho Chi Minh City, Viet Nam, in 2009. He is currently pursuing the M.S. degrees from the University of Technology, Ho Chi Minh City, Viet Nam.

His areas of interest include power distribution system analysis and network integration of distributed generation.

Phan Quoc Dung received his B.Eng degree in 1991 , received Ph.D degree in 1995 .He is an associate professor in the Power Delivery Engineering of Ho Chi Minh city University of Technology Viet Nam and lecturing in here from 1996. His research interests include: Power Electonic and Drive, Data mining

Le Dinh Khoa graduated from the University of Technology, Ho Chi Minh City, Viet Nam, in 2007, received the M.S. degrees from the University of Technology, Ho Chi Minh City, in 2009.

His areas of interest include power electronic, data mining and network integration of distributed generation.

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[ieee 2010 4th international power engineering and optimization conference (peoco) - shah alam,...

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