A novel optimal distribution system planning framework implementing distributed generation in a deregulated electricity market

Porkar, S.; Poure, Philippe; Abbaspour-Tehrani-fard, A.; Saadate, Shahrokh

doi:10.1016/j.epsr.2009.12.008

Cited by 87 publications

(39 citation statements)

References 17 publications

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“…To solve the cost function (Equation (4)) and the associated constraints (Equations (14)- (21)) as fast as possible, a user-friendly software package with interfacing between General Algebraic Modeling System (GAMS) [18] (especial tool for solving various types of optimization problems) and MATLAB (user friendly tool for using its visualization capabilities and easy data transfer), which has been proposed in References [17,18], is used.…”

Section: S Ij Smentioning

confidence: 99%

Optimal allocation of distributed generation using a two-stage multi-objective mixed-integer-nonlinear programming

Porkar

Poure²,

Abbaspour-Tehrani-fard

et al. 2010

Euro. Trans. Electr. Power

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SUMMARYCost is one of the most essential factors that influence many decisions taken in the distribution system planning. In general, cost can be defined as everything that should be sacrificed to gain the desired results. This paper proposes a new two-stage methodology for distributed generation (DG) placement as an attractive option for distribution system planner. This method aims to minimize cost and maximize total system benefit (TSB). Optimal placement and size are obtained from total cost minimization mathematical problem which is solved in the first stage. For each DG cost characteristics and for each investment payback time, there is an optimal location and size. Then the optimal DG investment payback time results from the TSB maximization problem, which is solved in the second stage. The various DG technologies offer the opportunity of selecting the right energy solution at the right location. Five types of DG are tested to give system deciders some choices. Different system conditions are simulated to illustrate the effect of DG installation on the distribution system as well as the ability of the proposed methodology. A user-friendly software package has been developed to solve efficiently and quickly the two optimization mathematical problems. The proposed methodology has been tested on IEEE 30-bus test system.

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Section: S Ij Smentioning

confidence: 99%

Optimal allocation of distributed generation using a two-stage multi-objective mixed-integer-nonlinear programming

Porkar

Poure²,

Abbaspour-Tehrani-fard

et al. 2010

Euro. Trans. Electr. Power

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“…Para la producción de electricidad, la GD puede utilizar fuentes de energía renovable (generación eólica, solar, biodiesel) o no renovable (generación con turbinas, microturbinas, motores de combustión. Dada su ubicación cerca de la demanda, la GD puede contribuir a la disminución de pérdidas activas y al mejoramiento del perfil de tensiones (Porkar et al, 2010). Adicionalmente, estudios recientes han mostrado que la GD puede traer otros beneficios como el aplazamiento de inversiones en la red (Gautam & Mithulananthan, 2007), el aumento en la confiabilidad (Jabr y Pal 2009) y la reducción de congestión (Liu et al 2011).…”

Section: Introductionunclassified

Ubicación de Generación Distribuida para Minimización de Pérdidas Usando un Algoritmo Genético Híbrido

Narváez¹,

López-Lezama

Velilla

2015

Inf. tecnol.

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ResumenEn este artículo se presenta una metodología para la ubicación óptima de Generación Distribuida (GD) usando un algoritmo genético híbrido. En este caso la función objetivo es la reducción de pérdidas. El algoritmo propuesto incorpora una red neuronal artificial para evaluar la función de adaptación y una búsqueda local que permite al algoritmo explorar un espacio de búsqueda más amplio. La contribución principal del artículo es la combinación de técnicas metaheurísticas con técnicas de inteligencia artificial para resolver un problema de optimización multi-modal y no convexo. La metodología propuesta es probada en un sistema de 34 barras mostrando que la ubicación apropiada de las unidades de GD permite reducir las pérdidas activas y mejorar el perfil de tensiones. Palabras clave: algoritmos genéticos, generación distribuida, redes neuronales artificiales Location of Distributed Generation for Power Losses Reduction using a Hybrid Genetic Algorithm AbstractThis paper presents a methodology for optimal Distributed Generation (DG) allocation using a hybrid genetic algorithm. In this case, the objective function is the minimization of power losses. The proposed algorithm involves an artificial neural network to evaluate the fitness function and a local search subroutine that allows the algorithm to explore a wider search space, and eventually, escape from local optimal solutions. The proposed approach was tested on a 34-bus system showing that the proper allocation of DG units allows the reduction of active power losses and the improvement of voltage profile.

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“…The impact of inherent time-varying behavior of the demand and distributed generation on distribution system performance using appropriate DGP techniques have been studied in (EL-Khattam et al, 2006;Rodriguez et al, 2009;Keane et al, 2009;Khoddr et al, 2010;Atwa et al, 2010). The optimal DGP has also been implemented in deregulated electricity market (Gautam et al, 2007;Singh et al, 2010;Porkar et al, 2010;Lezama et al, 2011). In literature, the single or multi objective functions have been considered with various constraints for DGP in order to meet the load demand with improved distribution system performance.…”

Section: Introductionmentioning

confidence: 99%

Review of distributed generation planning: objectives, constraints, and algorithms

Payasi¹,

Singh²,

Singh³

2011

Int. J. Eng. Sci. Tech

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The Distributed Generation (DG) technologies, which include both conventional and non-conventional type of energy sources for generating power, are gaining momentum and play major role in distribution system as an alternative distribution system planning option. The penetration of DGs is potentially beneficial if distributed generation planning (DGP) is optimal i.e. their site and size are selected optimally by optimization of single or multi-objective function under certain operating constraints. Many researchers have presented some rigorous optimization-based methods for DGP. This paper will review the various objectives, different constraints as well as optimization based models using conventional algorithms, intelligent searches and fuzzy set applications in DGP.

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A novel optimal distribution system planning framework implementing distributed generation in a deregulated electricity market

Cited by 87 publications

References 17 publications

Optimal allocation of distributed generation using a two-stage multi-objective mixed-integer-nonlinear programming

Optimal allocation of distributed generation using a two-stage multi-objective mixed-integer-nonlinear programming

Ubicación de Generación Distribuida para Minimización de Pérdidas Usando un Algoritmo Genético Híbrido

Review of distributed generation planning: objectives, constraints, and algorithms

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