Multicriteria Distribution Network Reconfiguration Considering Subtransmission Analysis

Bernardon, Daniel Pinheiro; Garcia, Vinícius Jacques; Ferreira, Adriana Scheffer Quintela; Canha, Luciane Neves

doi:10.1109/tpwrd.2010.2041013

Cited by 60 publications

(37 citation statements)

References 16 publications

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“…While duration is predominantly influenced by the distribution system structure (radial, meshed, weak meshed) and the existing automations, the frequency is mainly influenced by the adopted operational configuration; it can be minimized by the suitable choice of the effective configuration. In other words, through reconfiguration, we can improve those reliability indices which refer to the interruption frequency [32]. Otherwise, the reliability of a distribution system can be considered from two different angles:…”

Section: Reliability Of the Distribution Systemmentioning

confidence: 99%

“…This index can represent a possible objective and/or constraint in the optimization problem (because some customers can impose maximal/minimal limits in their supply contracts);  Reliability of the entire supply system: e.g., the number of interrupted customers per year [32], system average interruption frequency index (SAIFI) [35] (defined as: total number of customer interruptions longer than 3 minutes per total number of customers served).…”

Section: Reliability Of the Distribution Systemmentioning

confidence: 99%

“…With these approaches, linear programming cannot be used because we have more than one objective function. Thereby, different artificial intelligence based methods have been used: evolutionary [31], branch exchange [32] and particle swarm optimization [33].…”

Section: Introductionmentioning

confidence: 99%

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Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

et al. 2013

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Reconfiguration, by exchanging the functional links between the elements of the system, represents one of the most important measures which can improve the operational performance of a distribution system. The authors propose an original method, aiming at achieving such optimization through the reconfiguration of distribution systems taking into account various criteria in a flexible and robust approach. The novelty of the method consists in: the criteria for optimization are evaluated on active power distribution systems (containing distributed generators connected directly to the main distribution system and microgrids operated in grid-connected mode); the original formulation (Pareto optimality) of the optimization problem and an original genetic algorithm (based on NSGA-II) to solve the problem in a non-prohibitive execution time. The comparative tests performed on test systems have demonstrated the accuracy and promptness of the proposed algorithm.

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Section: Reliability Of the Distribution Systemmentioning

confidence: 99%

Section: Reliability Of the Distribution Systemmentioning

confidence: 99%

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Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

et al. 2013

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“…From a theoretical perspective, a network reconfiguration is an optimisation problem which may have different objective functions, such as minimum switching operations, minimum power loss, balanced feeder load balancing, or their combination [3][4][5][6][7][8][9] to comply with a set of operational constraints such as bus bar voltage limits, line or cable capacity ratings and fault levels. Generally these methods can be grouped into several categories; classic optimization technique [10][11][12][13], sensitivities analysis method [14], knowledge-based heuristic method [15][16][17][18], and Genetic Algorithms [19].…”

Section: Introductionmentioning

confidence: 99%

Distribution network reconfiguration validation with uncertain loads – network configuration determination and application

Bai

Mavrocostanti

Strickland

et al. 2016

IET Generation, Transmission & Distribution

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Automatic load transfer (ALT) on the 11kV network is the process by which circuit breakers on the network are switched to form open points in order to feed load from different primary substations. Some of the potential benefits that may be gained from dynamically using ALT include maximising utilisation of existing assets, voltage regulation and reduced losses. One of the key issues, that has yet to be properly addressed in published research, is how to validate that the modelled benefits really exist. On an 11kV distribution network where the load is continually changing and the load on each distribution substation is unlikely to be monitored -reduction in losses from moving the normally open point is particularly difficult to prove. This paper proposes a method to overcome this problem and uses measured primary feeder data from two parts of the Western Power Distribution 11kV Network under different configurations. The process of choosing the different configurations is based on a heuristic modelling method of locating minimum voltages to help reduce losses.

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“…Utilizing novel multi objective optimization approaches caused several progressions and remarkable achievements in various industrial branches [5][6][7][8]. But the main issue is that most of the multi objective methods like NSGA II, NSDE and so on, work on the basis of dominance concept and consequently their movements are toward the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

New Hybrid Non-Dominated Sorting Differential Evolutionary Algorithm

Bakhshipour¹,

Namdari²,

Bahador³

2016

Bulletin EEI

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Multicriteria Distribution Network Reconfiguration Considering Subtransmission Analysis

Cited by 60 publications

References 16 publications

Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

Distribution network reconfiguration validation with uncertain loads – network configuration determination and application

New Hybrid Non-Dominated Sorting Differential Evolutionary Algorithm

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