Optimal placement of switches in a radial distribution network for reliability improvement
Introduction
Distribution system reliability has proved to be of great concern in the present days of power system operation. With the deregulation of power system and enhanced competitive environment, the demand for uninterrupted quality power has increased. As distribution system has the greatest contribution to the interruption of supply to a consumer [1], hence, improving distribution system reliability is of serious concern in today’s power market. The enhancement of reliability always incurs a cost as it involves some additional preventive and corrective measures. So, the reliability improvement methods need to be adopted keeping in view the cost involved in the process. Failure rate, repair time and restoration time are some important parameters of defining reliability. Reducing the values of one or more of the above parameters can improve reliability considerably. Several approaches can be adopted to improve reliability, out of which, the present authors have adopted optimal placement of remote control switch (RCS) in the radial distribution network. RCSs are devices, which can isolate or connect a section of a network. Suitable locations of RCSs in a network may reduce the time to restore power and thus improve reliability. Placing one RCS at each segment of a network definitely improves reliability greatly, but at the same time it may incur a high installation and maintenance cost, as the number of RCSs required is large. Hence, a compromise is required, and here lies the importance of optimal allocation of RCSs. While adopting the present work, numbers of literatures have been reviewed in which similar type of work have been done. Some of these are briefly discussed here.
Bouhouras et al. [2] used an artificial intelligence technique with multi agent system for performing cost/worth assessment of reliability improvement in distribution networks. Haifenga et al. [3] adopted Monte-Carlo simulation based approach for providing a basis for using a parallel computing environment in power system reliability and cost evaluations. With the recent trend of automation, RCSs are gaining importance in reliability improvement studies. Some studies have been carried out in order to develop strategies for RCS without covering allocation of switches [4], [5].
Allocation of switches has been considered in [6], [7]. Optimal placement of switches and reclosers has been considered in [8], [9]. Bernardon et al. [10] proposed a methodology to consider the impact of RCS when computing the reliability indices and the algorithm for multi-criteria decision making to allocate these switches. Benavides et al. [11] proposed a new iterated sample construction with path relinking (ISCPR) to solve distribution system switch allocation problem. Zheng et al. [12] studied the quantitative impact of automatic switches on the reliability of power distribution systems. Esmaeilian and Fadaeinedjad [13] adopted a Binary Gravitational Search Algorithm (BGSA) for network reconfiguration and capacitor placement in distribution system with a view to improve reliability. Tippacon and Rerkpreedapong [14] adopted multi-objective ant colony optimization (MACO) whereas Pombo et al. [15] adopted a memetic algorithm combining Non dominated Sorting Genetic Algorithm II (NSGA-II) with a local search algorithm for switch and recloser allocation in order to minimize the reliability indices namely system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI) as well as the cost of equipments. Golestani and Tadayon [16] used Linear Fragmented Particle Swarm optimization for optimal switch placement in distribution system. Assis et al. [17] proposed a memetic algorithm based optimization methodology to sectionalizing, tie, manual, and automatic switches in distribution networks. Amanulla et al. [18] used binary particle swarm optimization-based search algorithm to find the optimal status of the switches in order to maximize the reliability and minimize the real power loss. Zou et al. [19] adopted methods including feeder reconfiguration, recloser installation, recloser replacement, and distributed generation (DG) installation to minimize SAIDI, an important reliability index. Brown et al. [20] used sequential feeder method and a multi-objective genetic algorithm (GA) together to solve the optimization of the feeder addition problem in an islanded distribution system with DGs. Vitorino et al. [21] presented the application of an improved genetic algorithm (IGA) to optimize simultaneously loss and reliability of a radial distribution system through a process of network reconfiguration as an optimization. Zhang et al. [22] proposed a reliability-oriented reconfiguration (ROR) method for improving distribution reliability and energy efficiency, based on interval analysis. Pfitscher et al. [23] presented a new methodology for automatic reconfiguration of distribution network, in order to improve network performance indicators, such as losses and reliability. Kavousi-Fard and Akbari-Zadeh [24] proposed a multi-objective distribution feeder reconfiguration problem for reliability enhancement as well as loss reduction. Raofat [25] adopted a GA based method to allocate DGs and RCSs simultaneously in order to reduce energy loss and improve reliability considering multilevel load.
Recently, Pinar Civicioglu [26] introduced a new algorithm named differential search (DS) algorithm to solve the problem of transforming geocentric cartesian coordinates into geodetic coordinates and compared its performance with classical methods and other computational intelligence algorithms. DS algorithm adopts the seasonal migration behavior of many organisms where they shift from one habitat to a more efficient one, in terms of efficiency of food areas. The individual organisms form a Superorganism which as a whole move toward more efficient area. The effectiveness of DS algorithm has already been compared with other algorithms like artificial bee colony algorithm (ABC), self-adaptive differential evolution algorithm (JDE), adaptive differential evolution algorithm (JADE), strategy adaptation based differential evolution algorithm (SADE), differential evolution algorithm with ensemble of parameters (EPSDE), gravitational search algorithm (GSA), particle swarm optimization (PSO) and covariance matrix adaptation evolution strategy (CMA-ES). DS algorithm has been found to solve the problem at a very high level of accuracy [26]. Unlike other algorithms like differential evolutionary algorithm (DE), JDE, and ABC, DS algorithm may simultaneously use more than one individual during updating steps. An important advantage of DS algorithm over many other algorithms is that DS algorithm has no inclination to correctly approach the best possible solution. Therefore, exploration ability of the algorithm is significantly improved compared to many other existing algorithms. Hence, it may be proved to be a successful strategy for solution of multimodal functions.
As DS algorithm has proved to be a new and effective evolutionary algorithm [26], the present authors have adopted this algorithm with a view to testing its computational efficiency to solve a multi-objective function in order to enhance system reliability at a reduced cost. The objective of this paper is to solve a multi-objective function in order to find a compromised solution both to enhance the reliability by optimal allocation of RCSs and minimize the cost incurred. In most of the previous work, where optimal placement of switches has been considered, number of RCS has been taken as fixed. In some literature where number of RCS has been considered as variable, multi-objective problem formulation has not been considered. In the present paper, both number and position of RCS has been considered as variable and a multi-objective function has been formulated. The outcome of the proposed technique has been compared with well established optimization techniques like particle swarm optimization (PSO), DE, GA, ant colony optimization (ACO) and GSA.
Section ‘RCS in radial distribution network and reliability indices’ of the paper provides a brief description of the function of RCS in radial distribution system and its impact on the reliability parameters. Section ‘Problem formulation’ describes mathematical formulation of the optimization problem. Section ‘Solution methodology using DS algorithm’ presents the DS algorithm and the steps involved to solve the optimal RCS allocation problem in order to enhance distribution system reliability. Simulation studies are presented and discussed in Section ‘Results and discussions’. The conclusion is drawn in Section ‘Conclusion’.
Section snippets
RCS in radial distribution network and reliability indices
With the recent trends of automation of distribution networks, RCS is proved to be very convenient as its switching time is very less. RCS may be sectionalizing switch (normally closed) or tie-switch (normally open). In the present work, the RCS considered for installation are normally closed type. In radial network, normally closed RCS can be operated to isolate a faulty section from the rest of the network. The location of RCS can contribute to enhance the reliability of a network to a great
Problem formulation
In this paper, the objective is to obtain the optimum number and location of RCS in radial distribution system. Increasing the number of RCS may reduce the EENS but at the same time, it may increase the cost involved. A multi-objective formulation is developed with a view to reduce the EENS cost as well as the RCS cost. Here, the target is to find a compromised solution such as to improve the reliability (by reducing equivalent cost of EENS) and at the same time reduce the cost of RCSs.
The
Solution methodology using DS algorithm
DS algorithm simulates the Brownian-like random-walk movement used by an organism to migrate [26]. Due to periodical climatic changes, many organisms show seasonal migration behaviour where they shift from one habitat to a more efficient one with respect to capacity and efficiency of food areas. In the process of migration, the species undergoing migration forms a Superorganism consisting of a large number of individuals and the Superorganism changes its position towards more fruitful areas.
The
Results and discussions
The DS algorithm has been implemented on four test systems and its performance has been compared with PSO, DE, GA, ACO and GSA for verifying its feasibility for solving optimization problems of distribution system reliability. The algorithms have been coded in MATLAB software (version 7.10.0) on a processor of specification Intel (R) Core (TM) i7-2600 CPU 3.40 GHz with 2 GB RAM.
Conclusion
In this paper, DS algorithm has been successfully implemented to find optimum number and location of RCS in a radial distribution feeder. The performance of DS algorithm has been compared with that of PSO, DE, GA, ACO and GSA. Both single objective and multi-objective formulation are considered and multi-objective formulation proves to provide more realistic solution set, by compromising reliability improvement with the cost incurred. Analyses of all the simulation results reveal that the
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