Optimal siting and sizing of distributed generation accompanied by reconfiguration of distribution networks for maximum loss reduction by using a new UVDA-based heuristic method

Bayat, Akbar; Bagheri, Amir; Noroozian, Reza

doi:10.1016/j.ijepes.2015.11.039

Cited by 138 publications

(93 citation statements)

References 29 publications

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“…In Figure 5, there is a comparison of the buses' voltage magnitudes and network losses for 4 different positions of DG units with constant DG% (45.27%) in the 33-bus system with middle load. Placements of DG units are assumed beginning of the feeder (bus 4), the middle of the feeder (bus 8), end of the feeder (bus 33), and in the feeder buses 15, 21, and 29 of Bayat et al 38 Voltage violation of lower limit (<0.95 p. u.)…”

Section: Dispersion Factor Of Dg Unitsmentioning

confidence: 99%

Quadratic optimization method for a dual index combination of the penetration level and the dispersion factor of the distributed generation

Naghdi

Shafiyi

Haghifam

2018

Int Trans Electr Energ Syst

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Summary Increasing integration of local generators is a challenge in the planning, design, and operation of the distribution system. The distributed energy resources, especially renewable ones, have caused dispersion of generation units along the feeder. This paper presents a dual index combination of the penetration level and the dispersion factor of distributed generation. Maximum penetration level of distributed generation was determined by a multiobjective function of the proposed dual index. This function has subroutines based on voltage stability, active and reactive power flows, voltage profile limits, and power loss that are restricted by technical constraints associated system with shunt compensator. A limited memory quasi‐Newton trust‐region algorithm was developed to optimize the objective function by using the first‐ and second‐order sensitivities of the nonlinear objective function and a trust region based on the constraints. Two networks were used for testing, and obtained results revealed accuracy and validity of the proposed method.

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Section: Dispersion Factor Of Dg Unitsmentioning

confidence: 99%

Quadratic optimization method for a dual index combination of the penetration level and the dispersion factor of the distributed generation

Naghdi

Shafiyi

Haghifam

2018

Int Trans Electr Energ Syst

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“…• Planificación coordinada: asociada a la reconfiguración de la red a través de la ubicación simultánea de GD, condensadores, dispositivos de protección y almacenadores de energía, considerando un determinado tiempo, en busca de mejorar las condiciones operativas del sistema [62]- [66].…”

Section: Investigaciones Futurasunclassified

Ubicación y dimensionamiento de generación distribuida: Una revisión

Grisales

Cuestas

Jaramillo

2017

Cien.Ing.Neogranadina

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La generación distribuida (GD) se presenta como un apoyo para suplir la demanda del sistema de forma localizada, lo que permite además como alternativas de generación el uso de fuentes de energía renovables a pequeña escala. Como aporte principal, este artículo presenta las consideraciones que se deben tener en cuenta al momento de analizar, modelar y solucionar el problema de ubicación y dimensionamiento de GD en la red eléctrica de distribución. Inicialmente, se enuncian los aspectos técnicos de la red, el modelado de las cargas e impactos de la GD; seguido de la formulación del modelo matemático del problema, principales técnicas de solución y metodologías aplicadas en la literatura; finalmente, se muestran las investigaciones futuras en este campo de estudio. Todo lo anterior en busca de mejorar las condiciones operativas de la red. Dentro de la revisión de los trabajos presentados por los diferentes autores, se encontró que una gran parte de los estudios se enfocan en los aspectos técnicos, sin tener en cuenta los costos asociados a la integración de GD, ni los incentivos económicos a los que puede ser susceptibles este tipo de proyectos, que son de vital importancia para evaluar la viabilidad de la propuesta de integración.

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“…To verify the effectiveness of the proposed method, four distribution networks have been used for testing including the 33bus, 84-bus, 119-bus, and 136-bus systems. e obtained results from the proposed CSFSA have been compared with those from other methods reported in the literature such as HSA [10], Firework Algorithm (FWA) [29], Runner-root Algorithm (RRA) [30], CSA [31], Hybrid Big Bang-Big Crunch Algorithm (HBB-BCA) [32], Fuzzy Shuffled Frog-Leaping Algorithm (Fuzzy-SFLA) [33], Multiobjective Invasive Weed Optimization (MOIWO) [34], Improved Adaptive Imperialist Competitive Algorithm (IAICA) [35], Improved Mixed-integer hybrid Differential Evolution (IMI-DE) [36], Plant Growth Simulation Algorithm (PGSA) [37], GA [38,39], hybrid Artificial Immune Systems-Ant Colony Optimization (AIS-ACO) [40], Heuristic method [41], improved Tabu search (ITS) [42], modified Tabu search (MTS) [43], hybrid Ant Colony Optimization-Harmony Search Algorithms (ACO-HAS) [44], adaptive GA (AGA) [45], Uniform Voltage Distribution-based constructive reconfiguration algorithm (UVDA) [46], Mixed-Integer Convex Programming (MICP) [47], Non-revisiting Genetic Algorithm (NRGA) [48], Feasibility-preserving Evolutionary Optimization (FPEO) [49], a hybridization of Grey Wolf Optimizer (GWO) and PSO method (GWO-PSO) [50], and Adaptive Shuffled Frogs Leaping Algorithm (ASFLA) [51] which are available in the literature. e remaining organization of the paper is represented in the order as follows.…”

Section: Introductionmentioning

confidence: 99%

Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm

Anh

2020

Complexity

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This paper proposes a chaotic stochastic fractal search algorithm (CSFSA) method to solve the reconfiguration problem for minimizing the power loss and improving the voltage profile in distribution systems. The proposed method is a metaheuristic method developed for overcoming the weaknesses of the conventional SFSA with two processes of diffuse and update. In the first process, new points will be created from the initial points by the Gaussian walk. For the second one, SFSA will update better positions for the particles obtained in the diffusion process. In addition, this study has also integrated the chaos theory to improve the SFSA diffusion process as well as increase the rate of convergence and the ability to find the optimal solution. The effectiveness of the proposed CSFSA has been verified on the 33-bus, 84-bus, 119-bus, and 136-bus distribution systems. The obtained results from the test cases by CSFSA have been verified to those from other natural methods in the literature. The result comparison has indicated that the proposed method is more effective than many other methods for the test systems in terms of power loss reduction and voltage profile improvement. Therefore, the proposed CSFSA can be a very promising potential method for solving the reconfiguration problem in distribution systems.

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Optimal siting and sizing of distributed generation accompanied by reconfiguration of distribution networks for maximum loss reduction by using a new UVDA-based heuristic method

Cited by 138 publications

References 29 publications

Quadratic optimization method for a dual index combination of the penetration level and the dispersion factor of the distributed generation

Quadratic optimization method for a dual index combination of the penetration level and the dispersion factor of the distributed generation

Ubicación y dimensionamiento de generación distribuida: Una revisión

Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm

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