Metaheuristic Optimization Methods for Optimal Power Flow Analysis in DC Distribution Networks

Abstract

In this paper is addressed the optimal power flow problem in direct current grids, by using solution methods based on metaheuristics techniques and numerical methods. For which was proposed a mixed integer nonlinear programming problem, that describes the optimal power flow problem in direct current grids. As solution methodology was proposed a master–slave strategy, which used in master stage three continuous solution methods for solving the optimal power flow problem: a particle swarm optimization algorithm, a continuous version of the genetic algorithm and the black hole optimization method. In the slave stages was used a methods based on successive approximations for solving the power flow problem, entrusted for calculates the objective function associated to each solution proposed by the master stage. As objective function was used the reduction of power loss on the electrical grid, associated to the energy transport. To validate the solution methodologies proposed were used the test systems of 21 and 69 buses, by implementing three levels of maximum distributed power penetration: 20%, 40% and 60% of the power supplied by the slack bus, without considering distributed generators installed on the electrical grid. The simulations were carried out in the software Matlab, by demonstrating that the methods with the best performance was the BH/SA, due to that show the best trade-off between the reduction of the power loss and processing time, for solving the optimal power flow problem in direct current networks.

Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

“…To validate the solution method proposed in this paper, it was compared with four other solution methodologies reported in the literature: PSO, BH, CGA, and ALO. They were selected due to their excellent performance and convergence in the solution to the OPF problem in DC networks [14,19]. All these methods employ a master-slave methodology.…”

“…Nevertheless, such methodologies should be validated in test systems of different sizes so that the effectiveness of the proposed solution methodology can be guaranteed in a network of any size. For example, in [14,19], the authors employed the master-slave methodology based on sequential programming to solve the OPF problem. Particularly in [19], three metaheuristic optimization algorithms were implemented as strategies to solve the OPF problem.…”

“…For example, in [14,19], the authors employed the master-slave methodology based on sequential programming to solve the OPF problem. Particularly in [19], three metaheuristic optimization algorithms were implemented as strategies to solve the OPF problem. First, the authors used a continuous genetic algorithm (CGA), which had been presented in [20].…”

“…The selection of the objective function depends on the system owner or the operator of the DC network, and it is subject to the set of constraints that compose DC networks in an environment of distributed generation. The objective function selected in this study is the minimization of the power losses in the network [19], which is presented below along with its set of constraints.…”

“…In order to calculate the v d voltage profiles, an iterative process should be implemented to find such values with an almost-null convergence error. For that purpose, a t counter should be added to Equation (19). As a result, the equation is:…”