Distribution System Reconfiguration for Loss Reduction Incorporating Load and Renewable Generation Uncertainties

Abbaskhani-Davanloo, Arash; Amini, Mahraz; Modarresi, Mohammad Sadegh; Jafarishiadeh, Fatemeh

doi:10.1109/tpec.2019.8662154

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…Over the past two decades, numerous methods have been proposed to address the DNR problem, a survey of which can be found in [33]. The uncertainty of load and generation is one of the critical factors that must be taken into account for DNR [1]. In [5], the uncertainty of renewable sources is modeled by 24-hour scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Optimization of Distribution Networks via Daily Reconfiguration

Razavi¹,

Momeni²,

Haghifam³

et al. 2020

Preprint

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This paper presents a comprehensive approach to improve the daily performance of an active distribution network (ADN), which includes renewable resources and responsive load (RL), using distributed network reconfiguration (DNR). Optimization objectives in this work can be described as (i) reducing active losses, (ii) improving the voltage profile, (iii) improving the network reliability, and (iv) minimizing distribution network operation costs. The suggested approach takes into account the probability of renewable resource failure, given the information collected from their initial state at the beginning of every day, in solving the optimization problem. Furthermore, solar radiation variations are estimated based on past historical data and the impact of the performance of renewable sources such as photovoltaic (PV) is determined hourly based on the Markov model. Since the number of reconfiguration scenarios is very high, stochastic DNR (SDNR) based on the probability distance method is employed to shrink the scenarios set. At the final stage an improved crow search algorithm (ICSA) is introduced to find the optimal scenario. The effectiveness of the suggested method is verified for the IEEE 33-bus radial distribution system as a case study.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Optimization of Distribution Networks via Daily Reconfiguration

Razavi¹,

Momeni²,

Haghifam³

et al. 2020

Preprint

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“…Se emplea un Algoritmo Evolutivo diferencial (DE) minimizando pérdidas de potencia, desviación de tensión, corriente límite de ramas y desbalance de corriente en [6]. En [7] se emplea un Algoritmo Big Bang-Big Crunch (HBB-BC) optimizando pérdidas de potencia y desviación de tensión, en tanto [8] utiliza un Algoritmo Genético (GA) y Lógica Difusa para la optimización de las mismas funciones objetivo. En [9] se propone un algoritmo híbrido novedoso para la reconfiguración dinámica y multiobjetivo de las redes de distribución utilizando el método de procesamiento paralelo y el enfoque de población adaptativa.…”

Section: Introductionunclassified

Reconfiguración multiobjetivo en sistemas de distribución primaria con presencia de generación distribuida

Cardero¹,

Fonseca²,

Torres³

et al. 2022

Ingeniare. Rev. chil. ing.

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Este trabajo aborda la reconfiguración multiobjetivo de las redes de distribución con presencia de generación distribuida. En el planteamiento del problema se trabaja con dos funciones objetivos; las pérdidas de potencia activa y el índice de tensión de la red. Mientras que en la solución del modelo se emplea un algoritmo evolutivo elitista de ordenamiento no dominado (NSGA-II), el que se implementa con ayuda del MATLAB, determinando un conjunto de soluciones factibles. La metodología propuesta es implementada en la reconfiguración del circuito de prueba, 33 nodos, 37 ramas y 4 generadores distribuidos, de la IEEE. Se analizan dos casos de estudios, uno sin presencia de generación distribuida y el otro en presencia de cuatro generadores distribuidos; se logra reducir las pérdidas de potencia de la red analizada y mejorar el perfil de tensión. Los resultados son comparados con otras investigaciones y métodos que han empleado el mismo circuito de prueba evidenciando su pertinencia.Palabras clave: Generación distribuida, NSGA-II, reconfiguración multiobjetivo de los sistemas de distribución.

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“…However, recently evolutionary techniques have become very popular in the optimization problems in power systems [19]. Optimization techniques such as genetic algorithms (GA), imperialistic competitive algorithm (ICA) and particle swarm optimization (PSO) have been successfully used to solve power optimization problems such as loss minimization, fault location estimation, and determining the number of protective and devices in power systems [20][21][22][23]. In [20], the authors utilized GA as a scenario reduction tool to implement a multi-objective optimization problem for loss minimization under DGs' uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…Optimization techniques such as genetic algorithms (GA), imperialistic competitive algorithm (ICA) and particle swarm optimization (PSO) have been successfully used to solve power optimization problems such as loss minimization, fault location estimation, and determining the number of protective and devices in power systems [20][21][22][23]. In [20], the authors utilized GA as a scenario reduction tool to implement a multi-objective optimization problem for loss minimization under DGs' uncertainty. In [21], the authors propose a new application of ICA for fault Iman Niazazari and Oveis Asgari Gashteroodkhani are with the Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV 89577 USA (e-mail: niazazari@nevada.unr.edu).…”

Section: Introductionmentioning

confidence: 99%

A Novel Economic Dispatch in Power Grids Based on Enhanced Firework Algorithm

Niazazari

Gashteroodkhani

Azari

2019

EJECE

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This paper proposes a novel single objective optimization technique for economic dispatch (ED) in power grids. This new technique is developed based on firework algorithm (FWA) and is implemented in the IEEE 24 bus reliability test system. In this paper, the single-objective enhanced fireworks (EFWA) is developed to find the economic operating condition to minimize the generation cost. This method is a swarm intelligence algorithm that solves a single-objective optimization problem much faster than other well-known algorithms such as genetic algorithm (GA). The experimental results show that the proposed EFWA method is indeed capable of obtaining higher quality solutions efficiently in ED problems.

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Distribution System Reconfiguration for Loss Reduction Incorporating Load and Renewable Generation Uncertainties

Cited by 8 publications

References 29 publications

Multi-Objective Optimization of Distribution Networks via Daily Reconfiguration

Multi-Objective Optimization of Distribution Networks via Daily Reconfiguration

Reconfiguración multiobjetivo en sistemas de distribución primaria con presencia de generación distribuida

A Novel Economic Dispatch in Power Grids Based on Enhanced Firework Algorithm

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