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.
This document presents a solution method for optimal power flow (OPF) problem in direct current (DC) networks by implementing a master-slave optimization methodology that combines an antlion optimizer (ALO) and a power flow approach based on successive approximation (SA ). In the master stage, the ALO determines the optimal amount of power to be delivered by all the distributed generators (DGs) in order to minimize the total power losses in the distribution lines of the DC network. In slave stage, the power flow problem is solved considering constant power loads and power outputs of DGs as constants. To validate the effectiveness and robustness of the proposed model, two additional comparative methods were implemented: particle swarm optimization (PSO) and black hole optimization (BHO). Two distribution test feeders (21 and 69 nodes) were simulated under different scenarios of distributed power generation. The simulations, conducted in MATLAB 2018$b$, show that the proposed method (ALO) presents a better balance between power loss minimization and computational time required to find the optimal solution regardless of the size of the DC network.
This paper presents the application of the backward/forward sweep iterative method for solving the power flow problem in direct current networks with radial structure, considering resistive and constant power loads. The validation of the effectiveness and robustness of the proposed method is made by using six comparative methods proposed in literature for power flow analysis in radial direct current networks: Gauss-Jacobi, Gauss-Seidel, Newton-Raphson, linear approximation based on Taylor series, successive approximations, and the triangular matrix formulation. Those methods are evaluated using four test systems formed by 10, 21, 33 and 69 nodes. Simulation results, obtained in MATLAB, show that the proposed approach is efficient for radial direct current grids in terms of solution quality and processing time, increasing the efficiency for larger number of nodes increases.
Due to the the lack of information about parameters in the datasheets of photovoltaic (PV) panels, it is difficult to study their modeling because PV behavior is based on voltage–current (V-I) data, which present a highly nonlinear relationship. To solve this difficulty, this study presents a mathematical three-diode model of a PV panel that includes multiple unknown parameters: photoinduced current, saturation currents of the three diodes, three ideality factors, serial resistance, and parallel resistance. These parameters should be estimated in the three-diode model of a PV panel to obtain the actual values that represent the voltage–current profile or the voltage–power profile (because of its visual simplicity) of the PV panel under analysis. In order to solve this problem, this paper proposes a new application of the salp swarm algorithm (SSA) to estimate the parameters of a three-diode model of a PV panel. Two test scenarios were implemented with two different PV panels, i.e., Kyocera KC200GT and Solarex MSX60, which generate different power levels and are widely used for commercial purposes. The results of the simulations were obtained using different irradiance levels. The proposed PV model was evaluated based on the experimental results of the PV modules analyzed in this paper. The efficiency of the optimization technique proposed here, i.e., SSA, was measured by a fair comparison between its numerical results and those of other optimization techniques tuned to obtain the best response in terms of the objective function.
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