Browsing by Author "Ramos-Paja C.A."

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A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systems
(Institute of Electrical and Electronics Engineers Inc., 2018) Grisales-Noreña L.F.; Montoya O.D.; González-Montoya D.; Ramos-Paja C.A.
This paper proposes a new approach for a Parallel implementation of Monte-Carlo method aimed for optimal location and sizing of distributed generators in distribution networks. In this approach, a reduction of the solution space is performed, using heuristic strategies, to improve processing times, power losses and voltage profiles considering the location of distributed generators in electric distribution networks. The mathematical formulation of the problem considers a single-objective function, which is composed by weighting factors associated with active power losses and square voltage error minimization; moreover, classical power flow constraints and distributed generation capabilities are considered as restrictions. A master-slave optimization strategy is used to solve the problem: the master stage corresponds to the proposed parallel Monte-Carlo with space solution reduction, which performs the optimal location of the distributed generators; the slave strategy is in charge of solving the resulting optimal power problem. Classical 33-node and 69node test systems are used to validate the proposed approach via MATLAB/MATPOWER software. For comparison purposes, the loss sensitivity factor (LSF), genetic algorithm (GA) and classical parallel Monte-Carlo (PMC) solutions are also tested. The simulations confirm that the proposed reduction to the space solution for the PMC provides improved results in comparison with the existing approaches. © 2018 IEEE.
Hybrid Metaheuristic Optimization Methods for Optimal Location and Sizing DGs in DC Networks
(Springer, 2019) Grisales-Noreña L.F.; Garzón Rivera O.D.; Montoya, Oscar Danilo; Ramos-Paja C.A.; Figueroa-Garcia J.C.; Duarte-Gonzalez M.; Jaramillo-Isaza S.; Orjuela-Canon A.D.; Diaz-Gutierrez Y.
In this paper is proposed a master-slave method for optimal location and sizing of distributed generators (DGs) in direct-current (DC) networks. In the master stage is used the genetic algorithm of Chu & Beasley (GA) for the location of DGs. In the slave stage three different continuous techniques are used: the Continuous genetic algorithm (CGA), the Black Hole optimization method (BH) and the particle swarm optimization (PSO) algorithm, in order to solve the problem of sizing. All of those techniques are combined to find the hybrid method that provides the best results in terms of power losses reduction and processing times. The reduction of the total power losses on the electrical network associated to the transport of energy is used as objective function, by also including a penalty to limit the power injected by the DGs on the grid, and considering all constraints associated to the DC grids. To verify the performance of the different hybrid methods studied, two test systems with 10 and 21 buses are implemented in MATLAB by considering the installation of three distributed generators. To solve the power flow equations, the slave stage uses successive approximations. The results obtained shown that the proposed methodology GA-BH provides the best trade-off between speed and power losses independent of the total power provided by the DGs and the network size. © 2019, Springer Nature Switzerland AG.
Linear power flow formulation for low-voltage DC power grids
(Elsevier Ltd, 2018) Montoya O.D.; Grisales-Noreña L.F.; González-Montoya D.; Ramos-Paja C.A.; Garces A.
This paper presents a reformulation of the power flow problem in low-voltage dc (LVDC) power grids via Taylor's series expansion. The solution of the original nonlinear quadratic model is achieved with this proposed formulation with minimal error when the dc network has a well defined operative conditions. The proposed approach provides an explicit solution of the power flow equations system, which avoids the use of iterative methods. Such a characteristic enables to provide accurate results with very short processing times when real operating scenarios of dc power grids are analyzed. Simulation results verify the precision and speed of the proposed method in comparison to classical numerical methods for both radial and mesh configurations. Those simulations were performed using C++ and MATLAB, which are programming environments commonly adopted to solve power flows. © 2018 Elsevier B.V.
Optimal Location of DGs in DC Power Grids Using a MINLP Model Implemented in GAMS
(Institute of Electrical and Electronics Engineers Inc., 2018) Montoya O.D.; Garrido Arévalo, Víctor Manuel; Grisales-Noreña L.F.; Gil-González W.; Garces A.; Ramos-Paja C.A.
This paper addresses the problem of optimal location and sizing of distributed generators (DGs) in direct-current (dc) power grids by using a mixed-integer nonlinear programming (MINLP) formulation. The reduction of the power losses in all branches of the network are considered as the objective function; while the restrictions are the power balance, voltage regulation, maximum penetration and maximum distributed generation units available. The general algebraic modeling system (GAMS) is selected as nonlinear optimizing package to solve this problem; besides, a small numerical example of energy production is introduced to illustrate the usability of using GAMS. Finally, a 21-node dc grid with two ideal generators, and multiple constant power loads, is used as test system. © 2018 IEEE.
Optimal Sizing of DGs in AC Distribution Networks via Black Hole Optimization
(Institute of Electrical and Electronics Engineers Inc., 2018) Montoya O.D.; Garrido Arévalo, Víctor Manuel; Grisales-Noreña L.F.; González-Montoya D.; Ramos-Paja C.A.
This paper presents a metaheuristic optimization technique named back hole optimization (BHO) for solving the problem of optimal dimensioning of distributed generation in radial distribution networks. This problem is formulated as a conventional optimal power flow problem in ac power grids. A master-slave methodology is proposed to solve this optimization problem. In the master stage the BHO technique decides the power output of each distributed generator (DG), while slave stage is responsible for solving the resulting power flow problem via classical sweep backward/forward technique. As comparison methods, classical particle swarm optimization as well as interior point methods are used. Two classical test systems with radial topologiesy and 33 and 69 nodes are used for numerical validations by using the MATLAB programming environment. Simulation results show the quality of the proposed optimization technique for power losses reduction in comparison with large-scale used optimization approaches available in specialized literature. © 2018 IEEE.