scholarly journals Optimal Power Dispatch of Dispersed Sources in Direct-Current Networks with Nonlinear Loads

2021 ◽  
Vol 2135 (1) ◽  
pp. 012009
Author(s):  
O. D. Montoya ◽  
V.M. Garrido ◽  
L.F. Grisales-Noreña
Keyword(s):  
Nonlinear Programming ◽  
Direct Current ◽  
Power Flow ◽  
Programming Model ◽  
Search Algorithm ◽  
Optimization Strategy ◽  
Nonlinear Loads ◽  
Optimal Power ◽  
Power Dispatch ◽  
Matlab Programming

Abstract The problem of the optimal power dispatch of dispersed generators in direct-current networks under the presence of nonlinear loads (constant power terminals) is addressed through a combinatorial optimization strategy by using a master-slave solution methodology. The optimal power generation in the dispersed is solved in the master optimization stage through the application of the vortex-search algorithm. Each combination of the power outputs at the dispersed generation sources is provided to a power flow methodology known as the hyperbolic power flow approach for direct current networks. The main advantage of the proposed optimization method corresponds to the possibility of solving a complex nonlinear programming problem via sequential quadratic programming, which can be easily implemented at any programming language with low computational effort and high-quality results. The computational tests of the master-slave optimization proposal are evaluated in a 21-bus system, and the numerical results are compared with the implementation of the exact nonlinear programming model in the General Algebraic Modeling System (i.e., GAMS). All the computational results are conducted through the MATLAB programming environment licensed by Universidad Tecnologica de Pereira for academic usage.

scholarly journals Application of the Crow Search Algorithm to the Problem of the Parametric Estimation in Transformers Considering Voltage and Current Measures

Computers ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
David Gilberto Gracia-Velásquez ◽  
Andrés Steven Morales-Rodríguez ◽  
Oscar Danilo Montoya
Keyword(s):  
Nonlinear Programming ◽  
Objective Function ◽  
Large Scale ◽  
Single Phase ◽  
Programming Model ◽  
Search Algorithm ◽  
Solution Space ◽  
Parametric Estimation ◽  
Solution Technique ◽  
Matlab Programming

The problem of the electrical characterization of single-phase transformers is addressed in this research through the application of the crow search algorithm (CSA). A nonlinear programming model to determine the series and parallel impedances of the transformer is formulated using the mean square error (MSE) between the voltages and currents measured and calculated as the objective function. The CSA is selected as a solution technique since it is efficient in dealing with complex nonlinear programming models using penalty factors to explore and exploit the solution space with minimum computational effort. Numerical results in three single-phase transformers with nominal sizes of 20 kVA, 45 kVA, 112.5 kVA, and 167 kVA demonstrate the efficiency of the proposed approach to define the transformer parameters when compared with the large-scale nonlinear solver fmincon in the MATLAB programming environment. Regarding the final objective function value, the CSA reaches objective functions lower than 2.75×10−11 for all the simulation cases, which confirms their effectiveness in minimizing the MSE between real (measured) and expected (calculated) voltage and current variables in the transformer.

Analysis of Optimal Power Flow Using Gravitational Search Algorithm

2016 ◽  
Vol 3 (4) ◽  
pp. 1-11
Author(s):  
M. Lakshmikantha Reddy ◽  
◽  
M. Ramprasad Reddy ◽  
V.C. Veera Reddy ◽  
◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimal Power ◽  
Gravitational Search

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

2021 ◽  
Vol 13 (16) ◽  
pp. 8703
Author(s):  
Andrés Alfonso Rosales-Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Objective Function ◽  
Successive Approximation ◽  
Direct Current ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Distributed Generators ◽  
Optimal Power ◽  
Power Flow Problem

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

scholarly journals Minimization of Risks of Voltage and Frequency Violations in Islanded Microgrids using Robust Probabilistic Optimal Power Flow

2021 ◽  
Author(s):  
Elton A. Chagas ◽  
Anselmo B. Rodrigues ◽  
Maria G. Silva
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Computational Cost ◽  
Forecast Errors ◽  
Droop Control ◽  
Control Parameters ◽  
Distributed Generators ◽  
Optimal Power

The main aim of this paper is to propose a robust probabilistic optimal power flow model to determine the droop control parameters for the Distributed Generators (DG) of a islanded microgrid. The term robust is related to the droop control parameters being immune to uncertainties associated with: load forecast errors, DG outages and variability of power output in renewable DG. This optimization problem is solved by an improved gravitational search algorithm (GSA). The test results demonstrated that the proposed method can achieve significant reductions in the load curtailments due to frequency and voltage violations. In addition, a comparison between GSA and the Particle Swarm Optimization (PSO) demonstrated that GSA is more suitable for evaluating the droop control parameters than PSO in relation to the computational cost and the optimal quality of the solution.

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