A New Method for Optimal RCS Placement in Distribution Power System Considering DG

Islanding Impact on Reliability

M. Tadayon, Student, Shiraz university/Power&Electronic, Shiraz, Iran and S. Golestani, Member, IEEE

AbstractRemote Control Switches (RCS) can improve

reliability of a power system by reducing the total time of fault detection, isolation and restoration. DGs can improve reliability of system if they can provide the loads after switches performance to isolate faults. Then places and sizes of DGs and places of switches have significant effect on reliability improvement. In this paper a dynamic preprocessed genetic algorithm (DAGA) is proposed to enhance benefit of switches in ENS reduction by minimizing the total cost of customer service outage and total cost of line switches in presence of DGs. The feasibility of the proposed algorithm is examined by application to several distribution systems.

Index TermsDispersed generation, Genetic algorithm,

Reliability, optimization method, switches

I. INTRODUCTION N a new competitive power systems the electric utility industry has confronted many challenges such as

adequate quality and reliability and to reduce its costs of operation, maintenance, and construction in order to provide lower rates for customers. Distribution networks as the final stage of power delivery have a key role in reliability improvement. DSP relays, Distributed generations and remote controllable switches are examples of new efforts to improve distribution reliability.

Reliability improvement and cost reduction are two important goals of utilities which are usually in opposite of each other. RCSs can improve reliability of a power system by reducing the total time of fault detection, isolation and restoration but they are very expensive therefore optimal allocation of switches have been the subject of many researches in last years [1-7] .

Genetic algorithm is used in [1] to find best location of RCSs. Simulated annealing [5], Immune algorithm [3], QEA based algorithm [7] are other meta heuristic methods to solve optimal switch placement problem.

In 1978, the Public Utilities Regulatory Policy Act (PURPA) allowed qualified facilities to generate and sell electricity, which the utility was obligated to purchase at its avoided cost. These small and scattered generators, referred to as distributed generation (DG). This is a recent technology which has a great effect on distribution network performance, operation and especially on systems reliability. [8]

When DG is connected to distribution system, the distribution system becomes a network where power sources are directly interlinked with consumers, and the power flow does not only flow from the bus of the substation to loads. Under such conditions, the fundamental changes of distribution system will occur. Therefore, the reliability of

distribution system with DG must be re-evaluated in order to confirm its affection on power quality and system security in the local area.

An island is a part of a power system, consisting of one or more power sources and load that is, for some period of time, separated from the rest of the system. After fault separation power system is divided to some zones by switches. If DG is capable to supply total loads in the island, it can improve reliability. Optimal location of switches will be affected by the location of DGs therefore in this paper an improved GA proposed to find the optimal location of RCSs in presence of DGs in distribution system.

There are many DG technologies that utilize both renewable energy (e.g., photovoltaic, wind, small hydro) and continuous fuel sources (e.g., reciprocating engines, microturbines, fuel cells). This paper does not focus on a specific technology.

In this paper a dynamic preprocessed genetic algorithm (DPGA) for optimal switch placement in distribution power system is illustrated. This method is a robust algorithm that finds best number and location of manual and remote controllable switches in reality condition in power distribution system. New operators and algorithm are used in proposed algorithm for enhance results.

II. DPGA ALGORITHM Expanse of search space in this problem needs to use

robust algorithm, therefore new dynamic operators and preprocessing methods are initiated to improve simple GA. In this section new algorithm is proposed with new operators and flowchart.

A. Preprocessor In the proposed algorithm, preprocessing is used to

determine good candidates for RCSs locations and summarize search space. The preprocessing stage will be terminated when the best chromosome repeats for predetermined enumeration iteration. This stage output is a list of good candidate nodes for RCS installation. These nodes will be searched with a more accurate algorithm in next stage to find the best locations.

B. Dynamic operators All simple genetic operators change chromosome

similarly in convergence procedure but in proposed dynamic operators, rate of modifications reduces in the direction of progress convergence procedure. With this modification in operators, generating useless and malformed chromosomes will be decreased.

I

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C. Perturbation operator If a solution obtained by some search

sensitive to small perturbations of its pamay not be good to use this solution in ceA similar situation is observed in Rpresence of DG where displacement of planning cost greatly. This paper provideof a new technique which extends the apdomains that require identification of robcall this new technique Perturbation opera

Small change in one or several switcheperformed in perturbation operator. It shthe rate of modifications reduces in the diconvergence procedure.

Fig 1. Flowchart of preproces

Fig 2. Flowchart of main proce

D. Chromosome definition In the proposed algorithm for optimal m

controllable switches placement each genstatus therefore fig 3 shows approprchromosomes.

Fig 3. Flowchart of main pro

Number of candidate locations 1 0 0 2 21 2 0 0 0

Size of population

techniques is very arameter values, it

ertain situations [9]. RCS placement in

one RCS changes s the basic concept

pplication of GA to bust solutions. We ator. es locations will be

hould be noted that irection of progress

ssor

essor

manual and remote ne consists of three riate definition of

ocess

1 0

In proposed chromosome def1 : suggests manual switch 2`: Suggests remote controlla0 : Non switch Best convergence has been s

preprocessing and main proceswhere population size is 40preprocessing is 80 and in maiWith little size of population, smaller than large population.

Fig 3. Procedure convergence in sim

Fig 4. procedure convergence in G

population

Fig 5. Preprocessing convergence

Fig 6. Main processing convergenc

III. REALITY CONDIn proposed algorithm for s

several aspects are introduced.

finition:

able switch

shown in fig 5 and 6 that are ssing in proposed algorithm

0 and iteration number in in processing is less than 50. time of an iteration is very

mple GA by population size 200

GA with dynamic operators by

n size 70

e in DPGA- Population size 40

ce in DPGA- Population size 40

DITION SIMULATION simulating reality conditions

A. Solve problem in presence of DG In recent years, DG has become an efficient and clean

alternative to traditional distribution systems. And recent technologies are making it economically feasible. Therefore new algorithms should be able to solve problems in presence of DGs. In proposed algorithm new method for reliability assessment is used for considering effect of DGs on reliability.

B. Solve problem with considering several switches in existent network For enhancing reliability indexes in existent distribution

network, considering several switches and protection devices is a important part of robust algorithm because in many networks displacing and deleting old equipments are not economical.

C. Solve problem with considering variety of installation cost of switches in different locations Installation cost of switches in different locations in

urban networks is various and affects best locations for switches in economic cost function.

D. Load priority Interruption cost depends on customers type, for example

hospital, commercial, residential and etc. Therefore ECost needs to consider load priority. In proposed algorithm suitable factors are used as load priority.

E. Manual and remote switches placement simultaneously Simultaneously placement of manual and remote

controllable switches decreases the total cost in life time.

IV. RELIABILITY ASSESSMENT CONSIDERING DG The Islanding operation scheme is dynamically formed

when fault occurs, which is based on the location of the fault and the actual status of distribution network operation before fault occurs [12]. In the paper, we assume that power of main power and DG can be supplied continuously and reliably.

Proposed algorithm in [10] is used for reliability assessment considering DG in distribution power system but only one DG was considered. For this purpose it is used all steps of this algorithm with a little change in formulation of calculating indices to consider only one DG in network.

In the proposed algorithm in [10] for each event of a contingency on any section,

a) Both circuit breakers at its ends should be opened immediately.

b) If the load demand in the island is beyond the DG capacity, DG is disconnected from the island instead of shedding some loads.

c) ENS is calculated in these conditions by usual methods.

Fig 7. Islanding phenomenon

, , (1)

(2)

Where Bi is the set of feeders sections between Li and DG;

, is the islanding probability of load point Li in the interval Tk

, is the sum of demands between load point Li and DG in the interval Tk ; is the failure rate of the system if there is no DG installed; Gk is generation of DG in the interval Tk;

V. MATHEMATICAL FORMULATION The objective function of proposed method includes total

cost of manual & remote controllable switches and cost of energy not supplied for each plan. For considering the priority of loads, weighted energy not supplied has been considered in Objective function. ECOSTi in (3) has been calculated by (4).

ECOST

PR RC MC (3)

Where LT is switches life time; RCk includes capital cost, installation cost and maintenance cost of remote controllable switches; MCk includes capital cost, installation cost and maintenance cost of manual switches; PRi is priority of load point i; ECOSTi is expected interruption cost of load point i; N is number of outages

ECOST L

. Cr. $ yr

Where

(4)

NC is number of contingencies that isolate load point i; Lij is curtailed load at load point i due to contingency j; rj is average outage time due to contingency ; is average failure rate of contingency ; Cji(rj) is outage cost in ($/yr) of load point i due to outage j with outage duration of rj that can be obtained from the CDF;

VI. METHOD APPLICATION In this section, the proposed method is applied to four

test systems to discuss how distributed generations and other reality conditions affect the result of switch placement in distribution power system. Table 1 shows constant factors in cost function and table 2 shows the results of 4 study cases.

Table 1. Quantity Variable name

12% Interest rate 10 Life time (y) 1.5 Interruption cost ($/kwh)

24000 RCS cost in expensive locations ($) 6000 Manual switches ($)

18000 RCS cost in cheap locations ($)

Fig 8. RBTS connected to BUS4

Results of proposed algorithm on 4 various conditions are compared in table 2. In this table :

1. Manual and remote controllable switch placement- manually change status needs 20 minutes. 2. Manual and remote controllable switch placement- manually change status needs 45 minutes. 3. Manual and remote controllable switch placement- manually change status needs 20 minutes in presence of one DG. 4. Manual and remote controllable switch placement in presence of one DG connected to high voltage side of transformer of LP23 and two existent switches in 59 and 60 - manually change status needs 20 minutes.

Table .2 Manual Remote Cost 1 8 , 22 , 54 , 33 , 61,62 - 1.694 e+8 2 30 , 33 , 22 , 61 8 , 54 , 62 1.713 e+8 3 8 , 22 , 54 , 33 ,61,62 46 1.676 e+8 4 33,30,6,62,54,46,59,60 - 1.668 e+8

The results of proposed algorithm on RBTS connected to BUS4 are compared with simulated annealing (SA) algorithm [5] and ternary particle swarm optimization algorithm [6]. It should be noted that in [5], only the best locations of sectionalizers are found and in [6] sectionalizers and breakers are optimized however in proposed algorithm number and location of manual and remote controllable switches for using as sectionalizers or breakers have been optimized.

Proposed algorithm is applied to RBTS connected to BUS4 test system in same conditions as [10] and results are compared in table 3. In this table :

1. Proposed algorithm without dynamic operators in same conditions as [10] 2. Proposed algorithm in same conditions as [10] 3. Result of [10]

Table .3 RCS location Total cost 1 24 , 21 , 61 , 33 , 30 , 60 , 46 , 4 , 8 , 62 , 55 , 52 , 14 9.0987 e+5

2 24 , 21 , 61 , 33 , 30 , 40 , 59 , 60 , 46 , 4 , 8 , 62 , 55 , 52 , 14 9.05 e+5

3 Between : 19,2021,2229,3031,3233,3435,363,47,89,1051,5253,5455,5657,5847,48 9.129 e+5

VII. CONCLUSION Dynamic operators, perturbation operator and

preprocessing stage have an important effect on procedure convergence and accuracy of results. A simulated reality condition in proposed algorithm makes this algorithm as a beneficial method for using in existent distribution networks. If number of outages is more than 500, proposed algorithm will need long runtime, therefore in new researches network decomposition has been suggested for reducing calculation time.

REFERENCES [1] B.Liu,S.Kun,J.Zou,X.Duan,X.Zheng "Optimal Feeder Switches

Location Scheme for High Reliability and Least Costs in Distribution System" Proceedings of the 6th World Congress on Intelligent Control and Automation, June 21-23,2006,Dalian,China

[2] Y.Mao,K.N.Miu"Switch Placement to Improve System Reliability for Radial Distribution Systems with Distributed Generation" IEEE Transactions on Power Systems,Vol.18,No.4,November 2003.

[3] C.H.Lin,C.S.Chen,H.J.Chuang,C.S.Li,M.Y.Huang,C.W.Huang"Optimal Switching Placement for Customer Interruption Cost Minimization" IEEE 2006

[4] P.M.S.Carvalho,L.A.F.M.Ferreira,A.J.Cerejo da Silva" A Decomposition Approach to Optimal Remote Controlled Switch Allocation in Distribution Systems" IEEE Transactions on Power Delivery,Vol.20,No.2,April 2005

[5] R.Billinton,S.Jonnavithula "Optimal Switching Device Placement in Radial Distribution Systems" IEEE Transactions on Power Delivery,Vol.11,No.3,July 1996.

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Mahdi Tadayon was born in Shiraz in the Islamic Republic of Iran, on May 24, 1982. He graduated from the Andisheh School, Shiraz, and received the MS.c degree from Shiraz University in 2007.His employment experience included the Shiraz Energy Consulting Engineers company, Shiraz, the BEHRAD Consulting Engineers Company, Isfahan. His special fields of interest include automation and loss reduction in power distribution systems. He received the Automation

Certification from Siemens Company in 2008.

Samaneh Golestani was born in abade in the Islamic Republic of Iran, on Feb 14, 1983. He graduated from the National Organization for Development of Exceptional Talent, Shiraz, and received the MS.c degree from Shiraz University in 2008. His employment experience included the Petrochemical Industries Design & Engineering Company (PIDEC), Shiraz, and Energy Industries Engineering & Design

(EIED), Shiraz. Her special fields of interest include Smart Grids, Power Generation Dispatch, and Power Transmission lines in bout conventional and new structured power market.