Journal of Renewable Energy

Volume 2015, Article ID 189080, 10 pages

http://dx.doi.org/10.1155/2015/189080

## Improved Cat Swarm Optimization for Simultaneous Allocation of DSTATCOM and DGs in Distribution Systems

Department of Electrical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur 302017, India

Received 9 June 2015; Revised 6 September 2015; Accepted 11 October 2015

Academic Editor: Joydeep Mitra

Copyright © 2015 Neeraj Kanwar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper addresses a new methodology for the simultaneous optimal allocation of DSTATCOM and DG in radial distribution systems to maximize power loss reduction while maintaining better node voltage profiles under multilevel load profile. Cat Swarm Optimization (CSO) is one of the recently developed powerful swarm intelligence-based optimization techniques that mimics the natural behavior of cats but usually suffers from poor convergence and accuracy while subjected to large dimension problem. Therefore, an Improved CSO (ICSO) technique is proposed to efficiently solve the problem where the seeking mode of CSO is modified to enhance its exploitation potential. In addition, the problem search space is virtually squeezed by suggesting an intelligent search approach which smartly scans the problem search space. Further, the effect of network reconfiguration has also been investigated after optimally placing DSTATCOMs and DGs in the distribution network. The suggested measures enhance the convergence and accuracy of the algorithm without loss of diversity. The proposed method is investigated on 69-bus test distribution system and the application results are very promising for the operation of smart distribution systems.

#### 1. Introduction

The electric power industries have witnessed many reforms in recent years. The existing distribution systems are moving towards smart distribution systems to achieve larger socioeconomic and other nontangible benefits. The rise of smart grid is a boon not only to society as a whole but also to all who are involved in the electric power industry, its customers, and its stakeholders [1]. Building of such distribution systems requires local generation of reactive and active power using distributed energy resources (DERs) such as Distribution Static Compensators (DSTATCOMs) and Distributed Generations (DGs). DSTATCOM is a power electronic-based synchronous voltage generator capable of providing rapid and uninterrupted capacitive and inductive reactive power supply [2]. Various renewable and nonrenewable DG technologies are available on the market today, such as microturbines, fuel cells, combustion gas turbines, photovoltaic, wind turbines, and combined heat and power [3]. The integration of renewable DG technologies such as photovoltaic and wind turbines is becoming more popular in distribution systems on account of smart grid initiatives and strict environmental laws. These components allow increased efficiency, more reliability, and better quality of electric service. Moreover, they also facilitate effective utilization and life extension of existing distribution system infrastructure [1]. However, optimal placement and sizing of these components are the important issues to extract maximum possible benefits.

The optimal allocation of DSTATCOM and DGs in distribution systems is a highly nonlinear complex combinatorial problem which has to satisfy various equality and inequality constraints. In the recent past, several population-based metaheuristic techniques such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Immune Algorithm (IA), Differential Evolution Algorithm (DEA), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Teaching-Learning Based Optimization (TLBO), Artificial Bee Colony (ABC), Harmony Search Algorithm (HSA), and Cuckoo Search Algorithm (CSA) have shown their potential to solve optimal DSTATCOM placement problem [4–7] or optimal DG placement problem [8–12]. However, the simultaneous placement strategy can independently set and control the real and reactive power flow in distribution networks [13].

A lot of research work has been carried out to successfully optimize the siting and sizing problems of active and reactive components when allocated separately. However, only a few researchers have attempted simultaneous placement strategy. References [14–17] have shown mutual impact of these components on the performance of distribution networks. Abu-Mouti and El-Hawary [14] employed an ABC algorithm to determine the optimal size of DGs, power factor, and location to minimize power losses. A heuristic approach is suggested by Naik et al. [15] where a node sensitivity analysis is used to identify the candidate DER sites, and their optimal capacities are determined by suggesting heuristic curve fitting technique. Moradi et al. [17] proposed a combined imperialist competitive algorithm- (ICA-) GA method to solve this multiobjective optimization problem. In this method, first ICA is used to find the sites and sizing of DERs and then the operators of GA are employed to further refine these solutions.

The smart grid requires integrated solutions for available distributed resources that reflect their coexistence to achieve higher efficiency through loss minimization and good quality power supply. Distribution networks are reconfigured frequently with changing operating conditions, and it is one of the effective means to improve their performance. The network reconfiguration is a process that alters feeder topological structure by managing the open/close status of sectionalizing and tie-switches under contingencies or normal operating conditions [18]. Changing network topology by reallocating loads from one feeder to another may balance loads among the feeders and decrease the real power losses [19]. Therefore, this is another resource that can be utilized in conjunction with simultaneous placement of DSTATCOM and DG. This approach has possibly not been attempted till date.

Cat Swarm Optimization (CSO) is one of the recently established high performance computational techniques introduced by Chu and Tsai [20]. CSO is inspired by the natural behavior of cats where two major behaviors of the cats are modeled into two submodels: seeking mode and the tracing mode. In the seeking mode, the cat looks around and seeks the next position to move to, whereas, in the tracing mode, the cat tracks some targets [21]. The important property of CSO is that it provides local as well as global search capability simultaneously [22]. It converges better and shows a better performance in finding the global best solution [21]. It has been successfully applied to solve diverse engineering optimization problems such as linear antenna array synthesis [23], deployment of wireless sensors [21], IIR system identification [24], clustering [25], and linear phase FIR filter design [26]. However, the exploration potential of CSO needs to be enhanced while subjected to large dimension problems by reviewing its seeking mode. Further, convergence and accuracy of the algorithm can be improved by suitably placing tentative solutions in the problem search space during the iterative process.

Several researchers [10, 12, 15, 27], and many others, have squeezed the problem search space by restricting the number of candidate locations for placing these devices. They generated a node priority list using certain node sensitivity-based approach and then selecting top few nodes from it as the candidate sites to allocate these devices. This approach drastically reduces the problem search space and also the CPU time incurred. However, the sensitivities are normally calculated for the base case conditions, where no such devices are installed [28]. Furthermore, when selecting only top nodes as the sensitive components, it did not give the true picture of the entire distribution network [29]. Therefore, such approaches are unreliable and thus lead the algorithm to suboptimal solution.

In light of the above discussion, a new Improved CSO- (ICSO-) based method is proposed for the simultaneous allocation of DSTATCOM and DGs in radial distribution networks. The objective is to maximize power loss reduction while maintaining a better node voltage profile. The distribution network is reconfigured after the optimal placement of these devices to extract maximum possible benefits. The seeking mode of CSO is modified to enhance exploitation potential of the algorithm. In addition, an intelligent search is proposed to enhance the overall performance of the optimizing tool.

The remainder of the paper is organized as follows: The problem is formulated in Section 2. The description of the standard and proposed CSO algorithm is presented in Sections 3 and 4, respectively. Section 5 deals with simulation results and the analysis of results is discussed in Section 6. Finally, the conclusions drawn from this work are presented in Section 7.

#### 2. Problem Formulation

The node voltage profile of distribution systems can be improved by installation of DERs as DGs and DSTATCOMs, network reconfiguration, tap changing transformers, and so forth. The proposed algorithm is installing these components, and then the distribution network is reconfigured. Therefore, a soft voltage constraint is treated as the part of objective function while installing DSTATCOM and DGs, and the solutions are accepted by imposing penalty so long as the voltage constraint violates within prespecified limits. However, a hard voltage constraint is necessary for the system operation. Thus it is employed while reconfiguring the distribution network after optimally placing these components. The amount of voltage profile improvement and the capacities of these components employed are not linearly related. Therefore, the advantage of soft voltage constraint employed in placing DSTATCOMs and DGs is that it results in lesser capacity allocation in the optimal solution. In other words, the whole burden of voltage profile improvement should not be imposed over these distributed components, as the network reconfiguration can successfully improve voltage profiles. Therefore, the objective function is formulated to maximize power loss reduction while maintaining a better node voltage profile by proposing a voltage penalty factor approach as defined below:where is the node voltage deviation penalty factor which is given bywheresubject to the following operational constraints.

*(a) Power Flow Equations*. The sum of the power purchased from utility grid and the total power generated by the different sources in the distribution system must be balanced by the local load demand and the power loss in the lines. For a radial network, a set of recursive equations are used to model the power flow in the network as shown by (4). A sample two-bus system including DG and DSTATCOM units is shown in Figure 1. Consider