Distributed Generation Impact Evaluation Using a Multi-Objective Tabu Search

Optimal Placement of Distributed Generation Using Bacterial foraging Optimization in an Electrical Distribution System

“…Chiradeja quantified the benefit of reduced line loss in a radial distribution feeder with a concentrated load [11]. Further, many researchers have used evolutionary computational methods for finding the optimal DG placement [14][15][16][17][18][19]. Mithulananthan used a genetic algorithm (GA) for placement of DGs to reduce the losses [15].…”

“…Mithulananthan used a genetic algorithm (GA) for placement of DGs to reduce the losses [15]. Celli and Ghiani used a multi objective Evolutionary algorithm for the sizing and placement of DGs [18]. Nara et al used a tabu search algorithm to find the optimal placement of DGs [19].…”

“…The process of optimization in a multi-objective scenario occurs in two stages: the determination of the solution set, where all objective function values of each solution cannot be enhanced at the same time and the human decision making whose criteria can be applied before, after or even during the optimization process. In [16] a distributed micro grid model has been introduced to optimize best location and the capacities within DG micro grid, in which wind power and photovoltaic power are taken into consideration with both PSO and Elitism Genetic Algorithm (EGA).A multi-objective indexbased approach for optimal location of DG and determining the size of DG units in electrical distribution systems with different load models based on PSO is introduced in [17,18] and a combined genetic algorithm is presented in [19] for optimal location and sizing of DG on DS. A methodology for evaluating the impact of DG-units on power loss, reliability, and voltage profile of distribution networks was presented in reference [11].…”

Coordinated Voltage Control in Distribution Network with the Presence of DGs and Variable Loads Using Pareto and Fuzzy Logic

“…Previous methods adequately solved the problem of MO voltage control problem using DGs in distribution networks obtaining optimum values of voltage and reactive power [3,4,10,16,[19][20][21][22][23]. There is no research that calculates the value of the reactive power of the DG using the optimal values of the MO voltage control problem in distribution network with variable and unbalanced loads.…”

Ubicación óptima de generación distribuida en sistemas de distribución usando un modelo de programación no lineal entero mixto

“…Si dicha metodología es fácil de implementar, su principal desventaja radica en que solo puede ser aplicada en alimentadores con distribución de carga uniforme, lo cual limita considerablemente su aplicación. Dado que el problema de ubicación óptima de GD es intrínsecamente no lineal y no convexo, este problema es comúnmente abordado usando técnicas de optimización metaheurísticas como los Algoritmos genéticos [7], Búsqueda Tabú [8] y Partículas Swarm [9]. El objetivo de este artículo es contribuir en esta línea de investigación, para lo cual se propone un modelo de programación no lineal entero mixto, que permite encontrar la ubicación óptima de varias unidades de GD en la red.…”