Multiobjective service restoration in distribution networks using an evolutionary approach and fuzzy sets

Augugliaro, A.; Dusonchet, L.; Sanseverino, Eleonora Riva

doi:10.1016/s0142-0615(99)00040-x

Cited by 53 publications

(31 citation statements)

References 10 publications

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“…These methods concern the development of specialised means of coding, reproduction methods based on domination and also use of co-evolutionary approaches. Several evolutionary algorithms have been developed to deal with distribution system reconfiguration problems [15,16,17,18]. Although the obtained results have been encouraging, the majority of evolutionary algorithms still demand high running time when applied to large-scale distribution systems.…”

Section: Methods Usedmentioning

confidence: 99%

Evolutionary approach to optimisation of the operation of electric power distribution networks

Stępień¹,

Filipiak²

2013

IJACSA

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Abstract-An idea of using a classifying system and coevolutionary algorithm for operation support of electric power distribution systems operators has been presented in the paper. The method proposed by the author of the work is typified by the short time of designating the most rational post breakdown configurations in complex electric power Medium Voltage distribution network structures. It is the use by the classifying system working with the co-evolution algorithm that enables the effective creation of substitute scenarios for the Medium Voltage electric power distribution network. The method drawn up may be used in current systems managing the work of distribution networks to assist network operators in taking decisions concerning connection actions in supervised electric power systems.

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Section: Methods Usedmentioning

confidence: 99%

Evolutionary approach to optimisation of the operation of electric power distribution networks

Stępień¹,

Filipiak²

2013

IJACSA

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“…Significant research has been devoted to solving the load restoration problem [16]- [25]. This problem has also been approached using the meta-heuristic algorithms [16],fuzzy technology [17], multi-agent technologies [18] and other non-structured methods [2]. Authors in [20] proposed a new approach based on optimal power flow technique to minimize customer load shedding aiming to improve load pickup.A software tool to simulate the recovery process of smart grid after an accordance of black out is presented in [21].…”

Section: Introductionmentioning

confidence: 99%

Load restoration methodology considering renewable energies and combined heat and power systems

Alwan¹,

Farhan²

2018

IJET

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Outages and faults cause problems in interconnected power system with huge economic consequences in modern societies. In the power system blackouts, black start resources such as micro combined heat and power (CHP) systems and renewable energies, due to their selfstart ability, are one of the solutions to restore power system as quickly as possible. In this paper, we propose a model for power system restoration considering CHP systems and renewable energy sources as being available in blackout states. We define a control variable representing a level of balance between the distance and importance of loads according to the importance and urgency of the affected customer. Dynamic power flow is considered in order to find feasible sequence and combination of loads for load restoration.

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“…In the last decades Evolutionary Algorithms (EAs) have been developed for Distribution System Reconfiguration (DSR) problems [1], [2]. The performance of EAs applied to DSR in general is superior in relation to approaches based on mathematical programming or traditional artificial intelligence techniques.…”

Section: Introductionmentioning

confidence: 99%

Combining subpopulation tables, non-dominated solutions and Strength Pareto of MOEAs to treat service restoration problem in large-scale distribution systems

Sanches

Mazucato²,

Castoldi

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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Abstract-The network reconfiguration for service restoration (SR) in distribution systems is a combinatorial complex optimization problem since it involves multiple non-linear constraints and objectives. For large networks, no exact algorithm has found adequate SR plans in real-time. On the other hand, methods combining Multi-objective Evolutionary Algorithms (MOEAs) with the Node-depth encoding (NDE) have shown to be able to efficiently generate adequate SR plans for large distribution systems (with thousands of buses and switches). This paper presents a new method that combining NDE with three MOEAs: (i) NSGA-II; (iii) SPEA 2; and (iii) a MOEA based on subpopulation tables. The idea is to obtain a method that cannot-only obtain adequate SR plans for large scale distribution systems, but can also find plans for small or large networks with similar quality. The proposed method, called MEA2N-STR, explores the space of the objectives solutions better than the other MOEAs with NDE, approximating better the Pareto-optimal front. This statement has been demonstrated by several simulations with DSs ranging from 632 to 1,277 switches.

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Multiobjective service restoration in distribution networks using an evolutionary approach and fuzzy sets

Cited by 53 publications

References 10 publications

Evolutionary approach to optimisation of the operation of electric power distribution networks

Evolutionary approach to optimisation of the operation of electric power distribution networks

Load restoration methodology considering renewable energies and combined heat and power systems

Combining subpopulation tables, non-dominated solutions and Strength Pareto of MOEAs to treat service restoration problem in large-scale distribution systems

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