Optimal recloser and autosectionalizer allocation in distribution networks using IPSO–Monte Carlo approach

https://doi.org/10.1016/j.ijepes.2013.10.012Get rights and content

Highlights

  • The recloser and autosectionalizer allocation is done for reliability improvement.

  • A methodology based on cost/benefit analysis for optimal allocation is proposed.

  • A hybrid method based on IPSO algorithm and Monte Carlo simulation is employed.

  • The effect of load types is considered in the optimal allocation.

  • The proposed algorithm is applied to the actual 50-bus system in Qazvin-Iran.

Abstract

Determination of optimal location of automatic devices such as reclosers and autosectionalizers (R&AS’s) for reliability improvement of distribution systems is a combinational and complex problem. In this paper, a methodology based on cost/benefit analysis for optimal R&AS’s allocation in distribution systems is proposed. The objective function is considered minimizing the costs of reliability of the distribution systems. Besides, the effect of load types is considered in the optimal R&AS’s allocation. A hybrid method based on Improved Particle Swarm Optimization (IPSO) algorithm and Monte Carlo simulation is proposed for solving the problem. The proposed algorithm is applied to the actual 50-bus distribution system in Qazvin-Iran. Numerical studies are representing of the effectiveness of the proposed method.

Introduction

Reliability improvement is one of the important goals for Distribution Companies. Use of automatic devices (recloser (RE) and autosectionlizer (ASE)) can reduce the amount of interrupted load during the faults and the restoration time.

An RE is an automatic protective device with the necessary intelligence to sense over currents, and to reclose automatically to reenergize the line. If the fault should be permanent, the RE will be opened after a preset number of operations (usually three or four) and thus isolate the faulted section from the main part of the system.

In overhead electric distribution systems, nearly 70% of faults are temporary faults, which are cleared by an upstream RE or breaker. The use of an RE on overhead distribution lines or a breaker with a reclosing relay at the substation significantly reduces the System Average Interruption (sustained interruptions) Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI) and Momentary Average Interruption Frequency Index (MAIFI).

ASE’s are designed for use on overhead distribution lines to improve reliability and service continuity. ASE’s operate in the same way as a fuse cutout. But unlike cutouts, the adjustable current and count operation allow the ASE’s to work in conjunction with the upstream circuit breaker or RE by counting the tripping operations to sectionalize permanent faults.

Distribution Companies use different methods for reliability improvement of distribution systems such as installation of switching devices (recloser, sectionlizer, etc.), installation of DG, and reconfiguration.

One of the protective devices that plays an important role in protection and reliability improvement of the overhead distribution lines is fuse cutout. In [1] a Markov model is used to allocate fuse cutouts in MV overhead lines by using the well-known reliability index of energy not supplied (ENS).

In [2] a procedure based on genetic algorithm for optimal allocation of sectionalizing switches in radial distribution systems is proposed. In this paper two types of optimization problems (allocation of sectionalizers when the tie-lines are already sited and optimal allocation of both the sectionalizers and tie-lines) are addressed. Ref. [3] proposes a methodology based on the optimization technique of simulated annealing to select the optimum number and the location of switches in a radial distribution system to minimize the outage costs, installation and maintenance. In [4], a value-based approach is proposed to take load distribution changes into account and search for new locations of feeder sectionalizers to enhance the reliability level in distribution system. Also authors of this paper in [5] propose the ant colony algorithm for optimum switch relocation.

One way to reduce the interruption costs is reducing the outage time by investing in facilities such as remote controlled switches (RCS) devices. Determining the optimal number of RCS devices and their optimal location with method based on decomposition and convex analysis is proposed in [6]. In [7] a new methodology based on the robust heuristic combinatory search algorithm for the determination of the optimal level of distribution network automation is proposed. The presented methodology considers scenarios with different types of automation equipment: local automation and remote control. Ref. [8] presents a methodology based on the analytic hierarchical process (AHP) method for allocation of remotely controlled switches in electric distribution systems.

Automatic switches play prominent rules in automated distribution systems. These devices automatically sectionalize faulted branches. Ref. [9] shows whether there are any differences in the placement decisions of automation devices, if the decision making is based on different criteria such as indices SAIFI, SAIDI, and CENS. In [10] an automatic calculation procedure based on Bellmann’s optimality principle is proposed for determination of the optimal number and position of automatic sectionalizing switching devices for both radial and meshed networks. In [11] the immune algorithm (IA), the optimal placement of both manual and automatic line switches to minimizing the total cost of customer service outage and investment cost of line switches is proposed. Moradi and Fotuhi-Firuzabad in [12] propose a novel three-state approach based on particle swarm to determine the optimum number and locations of sectionalizers and breakers in radial distribution systems. Ref. [13] proposes the modified shuffled frog leaping algorithm (SFLA) for the optimal placement of manual and automatic switches in distribution automation systems.

The type, number and location of the protective devices on a distribution feeder have a significant impact on improving the system reliability. Refs. [14], [15] use goal programming approach and Reactive Tabu Search (RTS) algorithm to optimal placement of protective devices on distribution feeders for a better operation and improvement of the reliability indices of the system. Ref. [16] presents a new explicit nonlinear binary programming model to allocate reclosers, fuses and sectionalizing switches to minimize the SAIDI and SAIFI of a radial distribution feeder.

One of the important effects of DGs on distribution networks is the improvement of network reliability. In Ref. [17] an expanded reliability model is presented to evaluate the impact of various types of DG on the reliability of supply in the industrial system.

In [18], [19] multi-objective planning (loss reduction, reliability improvement, etc.) for DG units allocation based on Improved Particle Swarm Optimization–Monte Carlo (IPSO–Monte Carlo) algorithm and non-dominated sorting genetic algorithm is proposed respectively. A GA-based method to allocate simultaneously DGs and remote controllable switches in electric distribution networks for reliability improvement and energy loss reduction is presented in [20]. Also Ref. [21] uses ant colony algorithm to placement of reclosers and distributed generators in distribution networks for reliability improvement. In [22] a procedure based on a custom-tailored genetic algorithm to find the optimal positions for DG and protection devices is presented for a feeder equipped with capacity-constrained distributed generations. A new approach based on dividing an existent distribution network into several zones is presented in [23] for the protection of distribution networks in the presence of DGs.

In this paper, an IPSO–Monte Carlo algorithm is proposed to find the location of R&AS’s. The objective function consists of minimization of the ENS costs which considering investment and maintenance costs. Reliability of distribution systems in the presence of R&AS’s during permanent and temporary faults is evaluated using Monte Carlo simulation (MCS). A multilevel yearly load model is utilized to achieve the optimal solution.

The rest of this paper is organized as follows: problem formulation is presented in Section 2. In Sections 3 Improved Particle Swarm Optimization (IPSO) algorithm, 4 The proposed R&AS’s allocation algorithm the improved practical swarm optimization algorithm and the proposed R&AS’s allocation algorithm are presented respectively. In Section 5, the simulation results are given, analyzed and discussed. Finally, the main conclusions of this paper are presented in Section 6.

Section snippets

Problem formulation

Costs and benefits of RE’s and ASE’s allocation in the network are explained in the following subsections.

Improved Particle Swarm Optimization (IPSO) algorithm

PSO is a search technique based on the sociality of bird blocks and fish schools with characteristics of high performance and ease of implementation. PSO technique finds the optimal solution using a population of particles. Each particle represents a candidate solution to the problem. Through the tracking of two best values, i.e. Pbest and Gbest, the global optimum may be achieved by this optimization technique. Pbest is the best value of the fitness function of every particle of the population

The proposed R&AS’s allocation algorithm

Fig. 1 shows the flowchart of the proposed R&AS’s allocation algorithm. The proposed algorithm, in this paper, is based on IPSO and Monte Carlo methods. This algorithm first proposed in [18] for DG allocation by the authors of this paper. Each particle consists of two strings. The first string indicates the location of RE; the second part indicates the location of ASE The length of each string is equal to the number of R&AS’s. In IPSO, first, N particles are randomly generated. In the first

Test system

The system under study is a practical medium-scale system of Qazvin Distribution Company in Iran which is shown in Fig. 2. The system has 50 buses and 49 lines. The permanent and temporary failure rates are shown in Table A1. Also the number of customers, average connected load to each bus and the percentage of load type (residential, industrial, commercial, etc.) in each load point are shown in Table A2.

Repair time of the overhead lines for this system is 2 h (hour). The required time for fault

Conclusion

In this paper, an IPSO–Monte Carlo algorithm for R&AS’s allocation in order to improve the reliability of distribution systems has been presented. The objective function is based on minimization of the costs related to ENS. The optimal allocation of R&AS’s under different scenarios, i.e., ASE allocation, RE allocation, and R&AS’s allocation is investigated.

As a general conclusion, one can say that the highest benefit from the installation and operation of the R&AS’s over their useful lifecycle

Acknowledgments

This study is the part of the research project “Optimal allocation of recloser and autosectionlizer on radial distribution lines”. No. 90/288, supported by Electric Power Distribution Company of Qazvin-Iran.

The authors would like to thank the respected authorities of the Imam Khomeini International University and Qazvin Distribution Company for financial support of the project. Also the authors would like to express their gratitude to Managing Director of Qazvin Distribution Company et al. for

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