Optimal Planning and Operation of Distribution Systems Considering Distributed Energy Resources and Automatic Reclosers

Grisales, Luis Fernando; Montoya, Oscar Danilo; Grajales, Alejandro; Isaza, Ricardo Alberto Hincapié; Echeverri, Mauricio Granada

doi:10.1109/tla.2018.8291464

Cited by 19 publications

(9 citation statements)

References 18 publications

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“…In all cases, the maximum penetration allowed for distributed generation was 40% of the power injected by the Slack node under non-distributed generation conditions. This limit was set to control the level of penetration following the recommendation given in [54]. Therefore, the main generator is always present at the Slack node.…”

Section: Performance Evaluation and Practical Testsmentioning

confidence: 99%

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

2018

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The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL) algorithm to locate distributed generators (DGs), and the use of Particle Swarm Optimization (PSO) to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL) algorithm and the PSO, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF), a Genetic Algorithm (GA) and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-PSO method provides the best balance between processing time, voltage profiles and reduction of power losses.Energies 2018, 11, 1018 2 of 27 generation [11,12]: reduction of power losses (due to transmission), improvement of voltage profiles and stability index, power factor enhancement, reduction of the harmonic distortion and increased line loadability, among others [13]. However, incorrect location or sizing procedures may result in voltage profiles out of conventional ranges, voltage fluctuations, line capacity violation, increased failure levels due to intermittent generation and higher costs associated to the DGs [14].Different methods have been developed to optimize the location and sizing of DGs. Those methods are aimed at reducing the computation time required and to improve the technical criteria of the grid, such as power losses, voltage profiles and power factor, among others [15,16]. For example, the work reported in [17] presents a hybrid method between the genetic algorithm proposed by Chu and Beasley (GACB) [18] and a heuristic approach to locate, select and size the feeders in a distributed generation environment. Such a strategy enables the reduction of the costs associated with the feeders (DGs) and the power losses, which is achieved by reducing the peak load using active power injection based on diesel-based DGs only. On the other hand, the work reported in [19] describes a multi-target approach based on the Particle Swarm Optimization (PSO) technique for locating the DGs, and an optimal flow analysis for sizing the DGs. That work considers several generators and different load models, thus enabling high penetration levels of distributed generation.A similar approach was presented in [20], which is a hybrid solution based on both the GACB and PSO. The main drawbacks of such a solution were the high level of power injection requested to the DGs and the lack of analysis of the computation time required by the proposed method. Another solution, based on the Loss ...

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Section: Performance Evaluation and Practical Testsmentioning

confidence: 99%

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

2018

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“…The test systems with 10 and 21 buses were taken from the literature [22,33]. The test systems with 33 and 69 buses are modifications of test systems used in AC networks [46][47][48]. The reactance in the lines and the reactive power consumed by the loads were eliminated to obtain the DC equivalent systems.…”

Section: Test Systemsmentioning

confidence: 99%

A Comparative Study on Power Flow Methods for Direct-Current Networks Considering Processing Time and Numerical Convergence Errors

et al. 2020

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This study analyzes the numerical convergence and processing time required by several classical and new solution methods proposed in the literature to solve the power-flow problem (PF) in direct-current (DC) networks considering radial and mesh topologies. Three classical numerical methods were studied: Gauss–Jacobi, Gauss–Seidel, and Newton–Raphson. In addition, two unconventional methods were selected. They are iterative and allow solving the DC PF in radial and mesh configurations. The first method uses a Taylor series expansion and a set of decoupling equations to linearize around the desired operating point. The second method manipulates the set of non-linear equations of the DC PF to transform it into a conventional fixed-point form. Moreover, this method is used to develop a successive approximation methodology. For the particular case of radial topology, three methods based on triangular matrix formulation, graph theory, and scanning algorithms were analyzed. The main objective of this study was to identify the methods with the best performance in terms of quality of solution (i.e., numerical convergence) and processing time to solve the DC power flow in mesh and radial distribution networks. We aimed at offering to the reader a set of PF methodologies to analyze electrical DC grids. The PF performance of the analyzed solution methods was evaluated through six test feeders; all of them were employed in prior studies for the same application. The simulation results show the adequate performance of the power-flow methods reviewed in this study, and they permit the selection of the best solution method for radial and mesh structures.

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“…Formalmente, sea A el conjunto de características de un modelo y x una característica dada. El tamaño de la entrada 1 Los resultados completos de la evaluación se encuentran en: https://github.com/jmhorcas/IEEE-energy-evaluation…”

Section: B Eficiencia De Los Algoritmos De Evoluciónunclassified

“…U NO de los beneficios de la computación en la nube es que permite ahorrar coste energético, ya que mediante la compartición de recursos y el almacenamiento masivo de datos reduce la necesidad de tener multitud de pequeños centros de datos. Sin embargo, en losúltimos años, el número de servicios ofrecidos por las plataformas en la nube (e.g., Microsoft Azure, Amazon Web Services), así como el número de usuarios de estos servicios ha aumentado enormemente, afectando considerablemente a la energía que consume el hardware ofrecido por estas plataformas [1]. De hecho, al integrar los servicios en la nube los desarrolladores están empezando a ser conscientes del gasto energético de estos servicios cuando son utilizados por gran cantidad de usuarios.…”

Section: Introductionunclassified

Automatic Evolution of Eco-Efficient Software Architectures with CVL Models

Horcas

Pinto

2020

IEEE Latin Am. Trans.

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Optimal Planning and Operation of Distribution Systems Considering Distributed Energy Resources and Automatic Reclosers

Cited by 19 publications

References 18 publications

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques

A Comparative Study on Power Flow Methods for Direct-Current Networks Considering Processing Time and Numerical Convergence Errors

Automatic Evolution of Eco-Efficient Software Architectures with CVL Models

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