This paper presents a metaheuristic algorithm, called BATp optimizer, to solve the combinatorial problem of static transmission networks expansion planning (STNEP) considering the effect of active power losses in the circuits. The optimizer is composed of two modules. One module generates candidate solutions, using the modified Bat Algorithm (BA), and the other that makes solutions with over costs or infeasibilities competitive. The modification made to the original BA consists in the inclusion of a local search intensification operator that acts on the elements of the current global optimal solution to improve the convergence rate and hinder stagnation in a suboptimal solution. The number of elements modified in the current global optimal solution is defined as a function of the number of buses and branches in the analyzed system. The size of the initial population is also defined as a function of the number of buses and branches. The active power losses are represented in the equality constraints of the mixed-integer nonlinear programming (MINLP) problem. The performance evaluation of the transmission network of the analyzed system is done by a linear power flow. The performance of the BATp optimizer was tested in three systems well known in the literature: the IEEE 24-bus and the South Brazilian -SB 46-bus. In each of the analyzed systems, situations were simulated with and without the possibility of generation redispatch. The BATp optimizer was able to find good results compared to those published in the literature, with relatively low computational effort.
In this paper, the African Buffalo Optimization (ABO) is adapted to solve the transmission network expansion static planning problem considering security restrictions (TNESPS). The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem. The ABO is based on the collective intelligence of the African buffaloes searching for food in the savannahs. The proposed algorithm uses the direct current model to represent the network, the transport model to generate the initial population, and two candidate solution improvement procedures, one being cost reduction and the other feasibility of infeasible solutions. The analysis of the specialized literature shows that the proposed algorithm has never been used to solve the static or dynamic TNESP problem, with or without security restrictions. Thus, this paper contributes to a new methodological approach to solving TNESPS problems. To evaluate the performance of the proposed algorithm, three systems that are often used in evaluations of new methodologies were used: Garver 6-bus system, IEEE 24-bus system and the South Brazilian 46-bus system.
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