# Optimal Placement of Multiple Feeder Terminal Units Using Intelligent Algorithms
study the RCS deployment problem, they focus on financial risk constraints [3]. [PERSON] et al. added the possible locations on the laterals to the solution of RCS placement problem [4], while [PERSON] et al. took the possible failure rate of the SS into consideration [5]. [PERSON] et al. emphasized the influence of RCS on the reliability of distribution network [6]. Moreover, scholars have continued relevant study in the presence of distributed generators recently [7; 8]. However, research on placement optimization of FTUs or DTUs is relatively rare. [PERSON] et al. presented a model of optimal location of remote terminal units (RTUs), which aims to minimize the total cost including the capital costs and the running cost. The node-voltage level is also added in the objective function [9]. [PERSON] et al. also studied this problem, but in the case of integrated cyber-physical distribution systems [10]. The switch with RTU installed in the above two articles is the type C switch in this article. Besides, the fault indicator (FI) placement has drawn attention, including new perspectives (such as multi-objective FI deployment optimization problem [11]), new scenarios (such as in cyber-enabled distribution network [12]) and new methods (such as a mixed integer programming (MIP) based approach [13]). However, there is a drawback which cannot be neglected with respect to practical distribution networks application. The existence of the type A, B, and C switches are not considered simultaneously in the above obtained solutions. Even though a distribution network installed with three types of FTUs is more likely to obtain a better compromise between reliability and economy. Moreover, traditional reliability algorithms are generally based on the assumption that all SSs are RCSs, so that there also exists a research gap in a tailored method for calculating the reliability of distribution network installed with three types of FTUs, to provide a more accurate evaluation for actual distribution network. Appropriate intelligent algorithms are also needed to be studied since the reliability of the distribution network is difficult to be formulated explicitly due to topology searching. Furthermore, optimal placement is normally a nonlinear non-convex NP-hard combinatorial optimization problem. Therefore, this paper proposes a novel mathematical model considering economic cost and electricity supply reliability from the perspective of life cycle cost (LCC). The novelty can be summarized as follows:  
* A reliability algorithm for the distribution network with three kinds of FTUs is proposed, by which the roles of three types of FTUs in the process of locating faults, isolating faults, and resuming power supply are clarified. To the best knowledge of the authors, this is also the first attempt to carry out a placement of the three FTUs simultaneously. * From the perspective of power supply service received by users, various evaluation indexes of reliability are considered. This ensures the high reliability after the model is applied to the distribution network. * In order to measure the loss of various customers caused by outage more accurately, the importance difference of several kinds of customers is considered in the calculation of outage losses. * A variety of intelligent algorithms are used and compared to get a satisfactory solution for the proposed model. This paper is structured as follows. By analyzing three kinds of FTUs' functions in the process of fault management, Section 2 proposes a reliability algorithm for the distribution network with three kinds of FTUs, which is the core content of the outage loss in the objective function. Section 3 provides a practical mathematical model for the FTUs configuration problem, in which both reliability and economy are considered. Subsequently, Section 4 explains the reason of choosing intelligent algorithms and describes how to use an colony optimization (ACO), discrete particle swarm optimization (DPSO), and genetic algorithm (GA) to solve the model.