Optimal Conductor selection and Capacitor Placement for Cost minimization in Distribution Systems 

Abstract

In this paper conductor selection and capacitor placement is done optimally to minimize system cost. Optimization problem is solved using Harmony search algorithm (HSA) with cost minimization as objective and maximum conductor current capacity as constraints. Both annual energy loss cost and annual capital investment cost for conductors and capacitors are considered for analysis. The proposed approach is implemented on an 85-bus system and results are presented. Results proved that selection of optimal conductor sizes and capacitors simultaneously for the network resulted in reduced losses, cost and improved voltage profile.

Toward Optimal Active Distribution Network Planning: A Critical Review of Conductor Size Selection Methods

“…This can be worst-case [20], annual [26], or multi-year [32]. Other studies that considered annual resolution include [45], [97], [98].…”

“…Initial costs for capacitor placement, losses as well as the cost of conductors are considered in [36], [47], [97]. Similar work is carried out in [52] to improve the reactive power planning capacity.…”

A MILP Model for Optimal Conductor Selection and Capacitor Banks Placement in Primary Distribution Systems

“…and 1.00 p.u., respectively. Table 11 presents the parameters used in [27] to compute the objective function. These parameters were also adopted in the present study to calculate objective functions f 1 and f 3 for comparative purposes.…”

“…Metaheuristics have been widely used to solve the OCS. The techniques reported in the specialized literature are: Evolutionary Strategies (ES) [14], Particle Swarm Optimization (PSO) [15][16][17][18], Differential Evolution Algorithm (DEA) [19,20], Genetic algorithm (GA) [10,[21][22][23][24][25], Adaptive Genetic Algorithm (AGA) [26], Harmony Search Algorithm (HSA) [27], Harmony Search Algorithm with a Differential Operator (HSDE) [28], Selective Particle Swarm Optimization (SPSO) [29], Discrete Genetic Algorithm (DGA) [30], Colonial Selection Algorithm (CSA) [31], Modified Differential Evolution Algorithm (MDEA) [32], Imperialism Competitive Algorithm (ICA) [17], Bacterial Foraging Algorithm (BFA) [33,34], Discrete Particle Swarm Optimization (DPSO) [35], Crow Search Algorithm (CSA) [36], Sine-Cosine Optimization Algorithm (SCA) [37], Tabu Search (TS) [38], Salp Swarm Optimization Algorithm (SSO) [39], Whale Optimization Algorithm (WOA) [40], Discrete Version of the Vortex Search Algorithm (DVSA) [41], and Newton's Metaheuristic Algorithm (NMA) [42]. Although these techniques are more sophisticated than heuristic approaches, they usually find approximate solutions and may not always find the true global optimum; besides, they require a significant amount of fine-tuning to achieve good performance, which can be time-consuming and require a great deal of expertise.…”

“…Therefore, combining these techniques may result in a more effectively planned system. The OCS problem has been combined with OPCB in several studies [16,[20][21][22][23]27,29,30,33,45], where the authors used metaheuristics to guide the search process. According to the literature survey, the integration of OCS and OPCB into a single mixed integer linear programming (MILP) problem, and its solution by using exact techniques and commercial software, has not been proposed yet.…”