Table Of ContentInternational Journal of Power System Optimization
January-June 2010, Volume 2, No. 1, pp. 71-79
Annealing Search Based Algorithm for Sectionalizing Switches
Placement on Radial Distribution Feeders for Reliability Enhancement
A. Y. Abdelaziz* & M. El-Shimy**
Abstract: The selection of an adequate number of switches and their location is a difficult task in distribution system planning.
However, Distribution feeder sectionalization plays a key role in reliability enhancement, balancing, loss reduction, and
automation of power distribution systems. The problem of placement of sectionalizing switches in radial distribution system
(RDS) falls into the class of discrete variable optimization problem with single minimum. This paper presents the use of
simulated annealing (SA) search algorithm in conjunction of previously published general-search technique to determine
the optimal number and location of sectionalizing switches in RDSs based on techno-economical feasibility maximization.
The problem formulation takes into account reliability, investment, and maintenance costs. The use of SA search greatly
reduces the number of candidate locations of the sectionalizing switches with respect to the standalone general search
technique. Optimal placement of sectionalizing switches is determined in a variety of distribution feeders. The results show
that the combined SA-search technique offers an accurate method for placemat of sectionalizing switches in distribution
systems.
Keywords: Radial Distribution System Reliability (RDSR), Customer Interruption Cost Reduction (CICR), Feasibility, General
Search Technique, Simulating Annealed.
1. INTRODUCTION transformed to systems with higher degree of reliability such
Two questions that often confront the power system design that techno-economical feasibility of sectionalizing switches
engineer are [1]: placement is maximized.
(i) What degree of reliability does the system have? In [5], a practical alternative to the customer damage
function method for customer outage cost estimation, as the
(ii) What do the varying degrees of reliability cost?
probability distribution approach is capable of recognizing
The answer to both these questions must necessarily the dispersed nature of the data using this approach the actual
involve the gathering and evaluation of considerable outage costs at the durations that were surveyed must be
statistical data to achieve compatibility between an represented by a common set of distribution that allow
acceptable degree of reliability and minimum economical interpolations to be made. In [6], two kinds of weakness in
cost. The point of concern is what is an acceptable degree traditional common cause outage models, which has been
of reliability? widely used for years, are identified. The first is ignorance
of the possibility of concurrence of common cause and
With the rising standards of living, the public is
independent outages. The second is computational
becoming more demanding with respect to the reliability of
complexity, for cases where there are multiple common
essential services. However increased reliability can only
cause outage groups in a system. A time sequential Monte
be obtained by increasing the cost of the services provided.
Carlo simulation technique for evaluating customer
The engineer therefore must necessarily seek a balance
unreliability costs is presented in [7]. The customer damage
between the increasing demands of reliability on the one
function (CDF) is combined with the time varying cost
hand, and increasing cost of achieving it on the other [1, 2].
weight factors to create a time varying cost model (TVCM)
The optimum selection of the number and location of for each individual customer.
sectionalizing switches is an important aspect of distribution
Several automated design methods for primary
system planning, design, and operation [3, 4]. A robust
distribution system are compared, and the algorithm best
general search technique was used in [3, 4] to find the
suited for distribution system reliability and cost
optimal number and location of sectionalizing switches on
optimization is identified. It is shown that reliability and
radial distribution systems. By this, radial distribution
cost create a new design criterion for primary distribution
systems which are sufferings from low reliability are
system [8]. A generalized analytical approach and a time
*,**Department of Electrical Power& Machines, Faculty of sequential Monte Carlo simulation technique for evaluating
Engineering, Ain Shams University, Cairo Egypt
the reliability cost/worth indices of a complex distribution
72 A. Y. Abdelaziz & M. El-Shimy
system with alternative supply are presented in [9]. Several 2. PROBLEM FORMULATION
load points and system reliability are calculated. Reliability
Consider a radial distribution system (RDS) with N possible
indices are used in comparing distribution system locations where sectionalizing switches can be placed. For
performance [9, 10]. The effects of manual and automatic fixed number of switches, there are numerous possible
sectionalized schemes on system reliability indices are location sets.
examined [10]. Value Based Reliability Planning (VBRP)
Although the General-search technique [3, 4] provides
method is applied to the problem of distribution substation
a robust and accurate tool for finding optimal number and
capacity enhancement [11] including detailed representation
location of sectionalizing switches in radial distribution
of performance during transformer failures and the cost of
systems, it suffers from high computational requirements,
customer interruptions. A multi-objective optimization
especially for large distribution systems. This is because the
methodology, using an evolutionary algorithm is presented
algorithm calculates the techno-economical feasibility for
in [12], for finding out the best distribution network
most of the possible location sets. In this paper possible
reliability while simultaneously minimizing the system
location sets are reduced via integrating simulated annealing
expansion costs. A nonlinear mixed integer optimization
search with the general-search technique.
model has been successfully used. To obtain high speed and
Simulating annealing searchis a Monte Carlo approach
efficiency of solution on both parallel platforms, two parallel
for reducing the number of candidate locations of the
methodologies, based on coarse grain asynchronous
sectionalizing switches with respect to the standalone
implementations, for composite reliability evaluation using
general-search technique. The term simulated annealing is
sequential Monte Carlo simulation are used in [13].
derived from the roughly analogues physical process of
Distribution system reliability as affected by protection heating and then slowly cooling a substance to obtain a
system failure is evaluated in [14]. Moreover, the usefulness strong crystalline structure.
of performing preventive maintenance on protection system
Mathematically, simulated annealing is a global search
is demonstrated.
technique in which a randomly- generated solution Y to a
Recently, several new algorithms and techniques are problem is compared to an existing solution X. The
used for optimal placement of sectionalizing switches in probability of Y of being accepted for investigation depends
distribution systems and for customer interruption cost on proximity of Y to X and the extent to which the solution
evaluation, some of them will be underlined here. Immune has been developed, as represented by the temperature which
Algorithm (IA) is used in [15] to drive the optimal placement reduces through the annealing process. Y is chosen to replace
of switching devices for distribution automation system to X according to probability function which depends on the
minimizing customer interruption cost and investment cost temperatures. X and Y must lie within the predefine search
of line switches. This Immune Algorithm is applied for space. The range indicates the extent of the search space.
optimal placement of switching devices in a practical The displacement is the distance between X and Y in the
distribution system of Taiwan Power Company (TaiPower). search space.
Ref. [16] presents a series of approximate methods to asses
An acceptance probability is defined as:
customer interruption cost evaluation on a distribution feeder
where estimation of the costs associated with widespread P = exp [– (Displacement/Range) (Ts/T)]
A
outage event in a way which reduces the required effort.
WhereT is the instantaneous temperature and Ts is the
Ref. [17] presents a distributed processing of reliability index
initial temperature. P is compared to a random value r in
assessment (RIA) for distribution systems, then proposes a A
the range [0-1]. Ifr >P , the solutionY is rejected and the
coarse–grained distributed processing scheme for RIA A
process is repeated. Ifr <P thenY is accepted for evaluation.
which is applied to reliability–based network reconfiguration A
(RN), which optimizes the system reliability based on Note for small values of displacement and high values
annealed local search. Ref. [18] presents some probabilistic of temperature, the acceptance probability is close to unity,
tools that can be used to deal with what is generally known so l solutions are more likely to be accepted for evaluation
as reliability worth that is, the benefits derived by the if they are close to the existing solution or if the temperature
investments aimed at increasing the reliability. The state is high. ThenY is accepted, andX is replaced byY.
space model made by independent semi-Markov
components allows a complete assessment of the electrical
3. CASE STUDY SYSTEMS
power system reliability. Ref. [19] presents a methodology
The proposed technique is applied to a variety of radial
that uses optimal planning to explore the space of energy
distribution feeders shown in Fig. 1 to 4. Data for feeders
costs and system parameters searching for optimal network
of Fig. 1 and 2 can be found in [3, 4]. Moreover, Data for
solutions. It results in establishing investment decision-
feeders of Fig. 3 and 4 can be found in [20].
making criteria for reliability adequacy in circuit backups.
Annealing Search Based Algorithm for Sectionalizing Switches Placement on Radial Distribution Feeders... 73
Fig. 1: (Feeder 1) x Candidate Locations of Proposed Switches
Fig. 2: (Feeder 3) x Candidate Locations of Proposed Switches
Fig. 3: (Bus 5) x Candidate Locations of Proposed Switches
74 A. Y. Abdelaziz & M. El-Shimy
Fig. 4: (Bus 6) x Candidate Locations of Proposed Switches
4. RESULTS technique gives identical results as the general search
Figures 5 to 8 show the optimal locations of sectionalizing technique [4]. A summary of techno-economical feasibility
switches on the case study systems. The proposed search study is shown in Tables 1, 2 and 3 for feeder of Fig. 3 and 4.
Fig. 5: (Feeder 1) Location of Sectionalizing Switches
Fig. 6: (Feeder 3) Location of Sectionalizing Switches
Annealing Search Based Algorithm for Sectionalizing Switches Placement on Radial Distribution Feeders... 75
Table 1 [Bus 5]
Set Locations CICR* $/Year Switches Cost ($) Feasibility $/year
1 12 26965 2500 24465
2 12, 1 51797 5000 46797
3 12, 1, 33 76600 7500 69100
4 12, 1, 33, 23 100890 10000 90890
5 12, 1, 33, 23,7 104250 12500 91750
6 12, 1, 33, 23,7,39 106930 15000 91930
7 12, 1, 33, 23,7,39,4 108930 17500 91430
* CICR is the Customer Interruption Cost Reduction [3,4]
Table 2
[Bus 6 (11kV) Section]
Set Locations CICR$/Year Switches Cost ($) Feasibility $/year
1 13 95666 2500 93166
2 13,1 178040 5000 173040
3 13,1,27 228010 7500 220510
4 13,1,27,31 230550 10000 220550
Fig. 7: (Bus 5) Location of Sectionalizing Switches
Fig. 8: (Bus 6) Location of Sectionalizing Switches
76 A. Y. Abdelaziz & M. El-Shimy
Table 3
[Bus 6 (33kV) Section]
Set Locations CICR$/Year Switches Cost ($/year) Feasibility$/year
35 1 1204939.5 2500 1202439.5
45 1,11 1242827.81 5000 1237827.81
44 1,11,10 1274635.62 7500 1267135.62
42 1,11,10,8 1301391.72 10000 1291391.72
40 1,11,10,8,6 1325995.04 12500 1313495.04
48 1,11,10,8,6,14 1348987.78 15000 1333987.78
46 1,11,10,8,6,14,12 1371099.72 17500 1353599.72
39 1,11,10,8,6,14,12,5 1391532.47 20000 1371532.47
49 1,11,10,8,6,14,12,5,15 1415400.38 22500 1392900..38
53 1,11,10,8,6,14,12,5,15,19 1431403.61 25000 1406403.61
38 11,10,8,6,14,12,5,15,19,4 1445816.33 27500 1418316.33
59 11,10,8,6,14,12,5,15,19, 4, 25 1445801.87 30000 1415801.87
54 11,10,8,6,14,12,5,15,19, 4,25,20 1478101.62 32500 1445601.62
60 11,10,8,6,14,12,5,15,19, 4, 25,20,26 1501601.29 35000 1469101.29
in the number of candidate locations on feeder 1 (of Fig. 1)
5. DISCUSSION
as Ts/T increased for fixed number of sectionalizing
The performance of the SA search is highly dependent on
switches.
Ts/T. This is illustrated in Fig. 9, which shows the reduction
Fig. 9a: Number of Reduction in Locations in Case of Using 1 Sectionalizing Switch
Fig. 9b: Number of Reduction in Locations in Case of Using 2 Sectionalizing Switches
Annealing Search Based Algorithm for Sectionalizing Switches Placement on Radial Distribution Feeders... 77
Fig. 9c: Number of Reduction in Locations in Case of Using 3 Sectionalizing Switches
Fig. 9d: Number of Reduction in Locations in Case of Using 4 Sectionalizing Switches
Fig. 9e: Number of Reduction in Locations in Case of Using 5 Sectionalizing Switches
Fig. 9f: Number of Reduction in Locations in Case of Using 6 Sectionalizing Switches
78 A. Y. Abdelaziz & M. El-Shimy
Fig. 9g:Number of Reduction in Locations in Case of Using 7 Sectionalizing Switches
By analysis of all the results of the four studied cases, Time Varying Load and Cost Models”,IEEE Transactions
it is found that value of Ts/T around 0.3 is suitable for all ,14, No. 3, July 1999, pp. 1046 -1051.
o n P o w e r D e liv e ry
feeders. Values of Ts/T greater than 0.3 may result in [8] R.E Brown, S. Gupta, R. D. Christie, S.S. Venkata and
rejection of some optimal locations from the candidate R. Fletcher, “Automated Primary Distribution Design
locations. Reliability and Cost Optimization”,IEEE Transactions on
Power Delivery, No. 2, April 1997, pp. 1017 1022.
6. CONCLUSIONAND RECOMMENDATIONS [9] Roy Billinton and Peng Wang, “Distribution System
Reliability Cost/Worth Analysis Using Analytical and
The use of SA greatly reduces the number of candidate Sequential Techniques”, IEEE Transactions on Power
locations of the sectionalizing switches with respect to the Systems,13, No. 4, November 1998, pp. 1245 -1250.
standalone general search technique. Optimal placement of
[10] N. E. chang, “Evaluate Distribution System Design By Cost
sectionalizing switches is determined in a variety of Reliability Indices”, IEEE Transactions on Power
distribution feeders. When using SA for determination of Apparatus and Systems,PAS 96, No. 5, September 1977,
optimal number and location of sectionalizing switches for pp. 1480 -1491.
any RDS, working at a value for temperature ratio (Ts/T) [11] Vito Longo and Walter R. Puntel, “Evaluation of
around 0.3 will be suitable as it gives maximum reduction Distribution System Enhancements Using Value Based
of candidate, and optimal location together. A value greater Reliability Planning Procedures”, IEEE Transactions on
than 0.3 will result in all locations including suitable Power Systems,15, No. 3, August 2000, pp. 1148-1153.
locations may be rejected. [12] Ignacio J. Ramirez- Rosado and Jose L. Bernal-Agustin,
“Reliability and Costs Optimization for Distribution
Networks Expansion Using an Evolutionary Algorithm”,
REFERENCES
IEEE Transactions on Power Systems,16, No. 1, February
[1] Uppal S. L., “Electric Power”, Khanna Publishers, 1998. 2001, pp. 111–118.
[2] Roy Billintion and Ronald N. Allan, “Reliability [13] Carmen L. T. Borges, Djalma M. Falcao, Joao Carlos
Assessment of Large Electric Power System”, 1988. O. Mello and Albert C.G. Melo, “Composite Reliability
[3] A.R. Abu EL-Wafa, M.M. EL-Shimy and A.Y. Abdelaziz, Evaluation By Sequential Monte Carlo Simulation on
“Optimal Number and Locations of Sectionalizing Devices Parallel and Distrbuted Processing Environments”, IEEE
in Medium Tension Radial Distribution System”,Scientific Transactions on Power Systems, 16, No. 2, May 2001,
Bulletin, Faculty of Engineering, Ain Shams Uni., 37, pp. 203-209.
No. 2, June 30, 2002, pp. 383-393. [14] J.J Meeuwsen, W. L. Kling and W. A. G. A. Ploem, “The
[4] Mohamed El-Shimy Mahmoud, “Effect of Recent Influence of Protection System Failures and Preventive
Equipment on Load Reliability “,M.Sc. Thesis, Faculty of Maintenance on Protection System in Distribution
Engineering, Ain Shams University, Cairo, Egypt, 2000. Systems”, IEEE Transactions on Power Delivery, 12,
No. 1, January 1997, pp. 125-133.
[5] R. Ghajar and R. Billinton, “Distribution Nature of
Residential Customer Outage Costs”, IEEE Transactions [15] Chao-Shun Chen, Chis-Hung Lin, Hui-Jen Chuang, Chung-
on Power Systems,11, No. 3, August 1996, pp. 1239 –1244. Sheng Li, Ming-Yang Huang, and Chia-Wen Huang,
“Optimal Placement of Line Switch for Distribution
[6] Weny Li and R. Billinton, “Common Cause Outage Models
Automation Systems Using Immune Algorithm”, IEEE
in Power System Reliability Evaluation”, IEEE
Transactions on Power Systems, 21, No. 3, August 2006,
Transactions on Power Systems, 18, No. 2, May 2003,
pp. 1209-1217.
pp. 966-968.
[16] Roy Billinton and Wijarn Wangdee, “Approximate Methods
[7] Peng Wang and Roy Billinton, “Time Sequential
for Event-Based Customer Interruption Cost Evaluation”,
Distribution System Reliability Worth Analysis Considering
IEEE Transactions on Power Systems,20, No. 2, May 2005,
Annealing Search Based Algorithm for Sectionalizing Switches Placement on Radial Distribution Feeders... 79
pp. 1103 -1110. [19] P.M.S. Carvalho and L.A. F. M. Ferreira, “Urban
[17] Fangxing Li, “Distribution Processing of Reliability Index Distribution Network Investment Criteria for Reliability
Assessment and Reliability-Based Network Reconfiguration Adequacy”, IEEE Transactions on Power Systems, 19,
in Power Distribution System”IEEE Transactions on Power No. 2, May 2004, pp. 1216-1222.
Systems,20, No. 1, February 2005, pp. 230-238. [20] Roy Billinton and Satish Jonnavithula, “A Test for Overall
[18] Antonio Pievatolo, Enrico Tironi, and Ivan Valade, “Semi- Power System Reliability Assessment”,IEEE Transactions
Markov Processes for Power System Reliability Assessment on Power Systems,11, No. 4, November 1996, pp. 1670-
with Application to Uninterruptible Power Supply” IEEE 1676.
Transactions on Power Systems, 19, No. 3, August 2004,
pp. 1326 -1333.
