An iterated sample construction with path relinking method: Application to switch allocation in electrical distribution networks

Benavides, Alexander J.; Ritt, Marcus; Buriol, Luciana S.; França, Paulo Morelato

doi:10.1016/j.cor.2012.05.006

Cited by 15 publications

(15 citation statements)

References 25 publications

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“…e.g. : The works [25, 32, 33, 52, 55, 65, 114] have a rather low complexity, since: a) they optimise only one PCD (sectionaliser) from the list of five possible PCD, b) they optimise only one design variable (location) from the list of four possible design variables, and c) they do not consider DG units. The work [89] has a high complexity, since: a) it optimises four PCD (sectionaliser, recloser, fuse, and fault indicator) from the list of five possible PCD, and b) they optimise three design variables (type, location, and time) from the list of four possible design variables. …”

Section: Comparison Of Methodsmentioning

confidence: 99%

Optimal allocation of protection and control devices in smart distribution systems: Models, methods, and future research

et al. 2021

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The fundamental goal of the distribution system operator (DSO) is to serve its customers with reliable and low-cost electricity. Failures in power distribution systems are responsible for 80% of customer service interruptions. The emergence of smart distribution system (SDS) with advanced distribution automation (DA) and communication infrastructure offers a great opportunity to improve reliability, through the automation of fault location, isolation, and service restoration (FLISR) process. DA includes the installation of protection and control devices (PCD). The use of PCD makes fault management more efficient, reduces average outage duration per customer in case of faults, reduces costs due to unsupplied energy, and improves distribution system reliability. Although the use of PCD remarkably enhances distribution system reliability, it is neither economical nor affordable to install them in all potential locations. To obtain the optimal allocation of PCD (OAPCD), an optimisation problem has to be formulated and solved. Several models and methods have been suggested for the OAPCD in SDSs. Herein, an overview of the state-of-the-art models and methods applied to the OAPCD in SDSs are introduced, identifying the contributions of reviewed works, identifying advantages and disadvantages, classifying and analysing current and future research directions in this area.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Comparison Of Methodsmentioning

confidence: 99%

Optimal allocation of protection and control devices in smart distribution systems: Models, methods, and future research

et al. 2021

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“…[Levitin et al(1994)Levitin, Mazal-Tov and Elmakis] introduced this optimization problem and proposed a genetic algorithm to allocate sectionalizers in a radial distribution network. Several other researchers followed the ideas of [Levitin et al(1994)Levitin, Mazal-Tov and Elmak in their proposals of metaheuristics for the SAP, such as simulated annealing [Billinton and Jonnavithula(1996)], iterated sample construction with path relinking [Benavides et al(2013) Benavides, Ritt, Buriol and França], memetic algorithm [Assis et al(2015)Assis, González, Usberti, Lyra, Cavellucci and Zuben], and bee colony algorithm [Aman et al(2016) Aman, Jasmon, Mokhlis and Bakar]. [Abiri-Jahromi et al(2012)Abiri-Jahromi, Fotuh proposed a mixed-integer linear model with an explicit enumeration of the locations of switches, resulting in an exponential number of variables and constraints.…”

Section: Illustrative Examples With Iflowsmentioning

confidence: 99%

Interruption flows for reliability evaluation of power distribution networks

Usberti¹,

Cavellucci²,

Filho³

2021

Preprint

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Energy networks should strive for reliability. How can it be assessed, measured, and improved? What are the best trade-offs between investments and their worth? The flow-based framework for the reliability assessment of energy networks proposed in this paper addresses these questions with a focus on power distribution networks. The framework introduces the concept of iflows, or interruption flows, which translate the analytical reliability evaluation into solving a series of node balance equations computable in linear time. The iflows permeate the network, providing relevant information to support linear formulations of reliability optimization problems. Numerical examples showcase the evaluation process obtained through iflows in illustrative distribution networks with distributed generation. A visual representation of the reliability state provides insights into the most critical regions of the network. A case study of the optimal allocation of switches in power distribution systems is described. Computational experiments were conducted using a benchmark of distribution networks, having up to 881 nodes. The results confirm the effectiveness of the approach in terms of providing high-quality information and optimal trade-offs to aid reliability decisions for energy networks.

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“…Owing to the combinatorial nature of the decision variables and the complex, non‐analytical characteristics of the objective function, metaheuristic algorithms have been the most popular choice for solving the switch allocation problem in a reasonable amount of time and with constrained computational resources. Metaheuristics such as simulated annealing [4], genetic algorithms [5, 6], immune algorithms [7], trinary particle swarm optimisation [8, 9], ant colony optimisation [10–12], memetic algorithms [13], tabu search [14, 15], shuffled frog‐leaping algorithm [16], and greedy randomised adaptive search procedure (GRASP) [17] have been successfully used to provide quality solutions for the allocation problem, even for large EDS. In addition, other specialised optimisation strategies have been proposed to solve simplified cases of the switch allocation problem, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Customer‐oriented reliability indices such as SAIDI and SAIFI have been explicitly minimised by authors in [4–6, 10, 21, 22]. Other reliability indices considered in the specialised literature are the EENS [4, 11, 13, 17, 18], the Expected Outage Cost (ECOST) [4, 7–9, 20], and other reliability costs associated with customer interruptions and unsupplied demand, calculated for each allocation proposal [7, 12, 14–16, 19]. Furthermore, most authors have also considered in the objective function the cost of purchasing, installing, and maintaining the allocated switches, i.e.…”

Section: Introductionmentioning

confidence: 99%

Optimisation‐based switch allocation to improve energy losses and service restoration in radial electrical distribution systems

López

Rider

2016

IET Generation, Transmission & Distribution

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An iterated sample construction with path relinking method: Application to switch allocation in electrical distribution networks

Cited by 15 publications

References 25 publications

Optimal allocation of protection and control devices in smart distribution systems: Models, methods, and future research

Optimal allocation of protection and control devices in smart distribution systems: Models, methods, and future research

Interruption flows for reliability evaluation of power distribution networks

Optimisation‐based switch allocation to improve energy losses and service restoration in radial electrical distribution systems

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