Optimal DG placement in deregulated electricity market

Gautam, Durga; Mithulananthan, Nadarajah

doi:10.1016/j.epsr.2006.11.014

Cited by 214 publications

(71 citation statements)

References 5 publications

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“…In particular, optimal siting and sizing of DGs with the goal of minimizing network losses has been addressed in [11]. In [12], the problem of optimal DG placement is investigated within the specific context of deregulated electricity markets with the aim of maximizing the social welfare and profits of DG owners. A multi-objective approach taking into account investment and maintenance of DG together with network operation, losses, and capacity adequacy costs is used in [13] and [14] for the optimal siting and sizing of DGs.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

Nick

Cherkaoui

Paolone

2014

IEEE Trans. Power Syst.

374

221

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Abstract-Dispersed storage systems (DSSs) can represent an important near-term solution for supporting the operation and control of active distribution networks (ADNs). Indeed, they have the capability to support ADNs by providing ancillary services in addition to energy balance capabilities. Within this context, this paper focuses on the optimal allocation of DSSs in ADNs by defining a multi-objective optimization problem aiming at finding the optimal trade-off between technical and economical goals. In particular, the proposed procedure accounts for: 1) network voltage deviations; 2) feeders/lines congestions; 3) network losses; 4) cost of supplying loads (from external grid or local producers) together with the cost of DSS investment/maintenance; 5) load curtailment; and 6) stochasticity of loads and renewables productions. The DSSs are suitably modeled to consider their ability to support the network by both active and reactive powers. A convex formulation of ac optimal power flow problem is used to define a mixed integer second-order cone programming problem to optimally site and size the DSSs in the network. A test case referring to IEEE 34 bus distribution test feeder is used to demonstrate and discuss the effectiveness of the proposed methodology.Index Terms-Ancillary services, energy storage, mixed integer second order cone programming, power distribution, power system planning. NOMENCLATURE: DSSDispersed storage system. DG Distributed generation.Variables: 1 Binary variable associated with the presence of energy storage at bus .

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Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

Nick

Cherkaoui

Paolone

2014

IEEE Trans. Power Syst.

374

221

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“…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). Sin embargo, los beneficios de la GD dependen en gran medida de su ubicación y dimensionamiento.…”

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 first group discusses the matter of DG size and allocation taking economic aspects into consideration using sequential quadratic programming (SQP) algorithm [7]. Meanwhile, Durga and Mithulananthan consider the DG existence in the corridor of deregulated electricity market [8]. However, all of the above mentioned efforts tried to solve the DG size and allocation problems based on analytical method and only able to provide a single solution for their objective studies.…”

Section: Introductionmentioning

confidence: 99%

Multi-Deployment of Dispersed Power Sources Using RBF Neural Network

Qudaih¹,

Hiyama²

2010

EPE

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Multi-deployment of dispersed power sources became an important need with the rapid increase of the Distributed generation (DG) technology and smart grid applications. This paper proposes a computational tool to assess the optimal DG size and deployment for more than one unit, taking the minimum losses and voltage profile as objective functions. A technique called radial basis function (RBF) neural network has been utilized for such target. The method is only depending on the training process; so it is simple in terms of algorithm and structure and it has fast computational speed and high accuracy; therefore it is flexible and reliable to be tested in different target scenarios. The proposed method is designed to find the best solution of multi-DG sizing and deployment in 33-bus IEEE distribution system and create the suitable topology of the system in the presence of DG. Some important results for DG deployment and discussion are involved to show the effectiveness of our proposed method.

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Optimal DG placement in deregulated electricity market

Cited by 214 publications

References 5 publications

Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

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

Multi-Deployment of Dispersed Power Sources Using RBF Neural Network

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