Distribution system reconfiguration for loss reduction: an algorithm based on network partitioning theory

Sarfi, R.J.; Salama, M.M.A.; Chikhani, A.Y.

doi:10.1109/59.486140

Cited by 75 publications

(16 citation statements)

References 6 publications

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“…In [2], by optimal distribution system configuration, the lowest current is determined by the optimal power flow method. Other techniques like Quadratic programming [3] and network partitioning techniques [4], a heuristic nonlinear constructive method [5] are used in earlier stages. These methods find admirable solutions for the medium size systems and are not suitable for large systems [6].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective distribution feeder reconfiguration by considering energy not supplied with distributed generation

2015

Teh. vjesn.

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Original scientific paper Nowadays, the operational performance of the Distribution system can be improved by exchanging the functional links between the elements of the system, called reconfiguration. This paper aims at achieving such optimization through the reconfiguration of distribution systems taking into account various criteria. The newness of the method is in the fact that optimization is evaluated on active power distribution systems i.e by incorporating the distributed generators directly to the main distribution system. This paper presents a proficient method for solving the multi-objective reconfiguration of radial distribution systems with regard to distributed generators. The conformist distribution feeder reconfiguration (DFR) problem cannot meet the reliability requirements. The recommended approach considers reliability (Energy Not Supplied), voltage deviation and power loss problem simultaneously. By taking the reliability objective into account, the DFR problem becomes more complicated than before and it needs to be solved with a precise algorithm. Therefore this study utilizes Non Dominated Sorted Genetic Algorithm-II (NSGA-II) for DFR and Gravitational Search Algorithm (GSA) for DG placement is to find the optimal location and sizing of DGs which minimizes real power loss, Energy Not Supplied (ENS) and improves voltage profile of the network. Keywords: distributed generation; Distribution Feeder Reconfiguration (DFR); Energy not Supplied (ENS); GSA; NSGA-II Rekonfiguracija multi-objektivnog distribucijskog napojnog voda s obzirom na neisporučenu energiju distribuirane proizvodnjeIzvorni znanstveni članak Danas se učinkovitost distribucijskog sustava može poboljšati zamjenom funkcionalnih poveznica između elemenata sustava, to jest rekonfiguracijom. Cilj je rada postići takvu optimizaciju rekonfiguracijom distribucijskog sustava uzimajući u obzir različite kriterije. Novina ove metode je u činjenici da se optimizacija procjenjuje na aktivnim sustavima raspodjele energije tj. ugrađivanjem distribuiranih generatora direktno u glavni distribucijski sustav. U radu se predstavlja napredna metoda za rješavanje multi-objektivne rekonfiguracije sustava radijalne distribucije u odnosu na distribuirane generatore. Konformistički promatran problem rekonfiguracije distribucijskog napojnog voda (DFR) ne može zadovoljiti zahtjevima za pouzdanošću. Preporučeni pristup uzima simultano u obzir problem pouzdanosti (neisporučena energija), devijacije u naponu i gubitka energije. Uzimajući u obzir pouzdanost, DFR problem postaje složeniji nego ranije i treba se riješiti preciznim algoritmom. Stoga se u ovom radu koristi Non Dominated Sorted Genetic Algorithm-II (NSGA-II) za DFR i Gravitational Search Algorithm (GSA) za optimalnu lokaciju i veličinu DG-ija jer se tako minimalizira stvarni gubitak energije, neisporučene energije (ENS) i poboljšava profil napona mreže.Ključne riječi: distribuirana proizvodnja; neisporučena energija (ENS); GSA; NSGA-II; rekonfiguracija distribucijskog napojnog voda (DFR)

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Section: Introductionmentioning

confidence: 99%

Multi-objective distribution feeder reconfiguration by considering energy not supplied with distributed generation

2015

Teh. vjesn.

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“…This category includes valuable past procedures such as the Branch Exchange Method [1,2] and Sequence Switch Operation Method (SSOM) [3,4] or further improved techniques such as the Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) based and other heuristic methods such as in Refs. [5][6][7][8][9][10]. The general procedure in the Branch Exchange Method starts with closing all network switches to perform a mesh configuration.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary approach for optimal time interval determination in distribution network reconfiguration under variable load

Milani

Haghifam

2013

Mathematical and Computer Modelling

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a b s t r a c tPower loss is one of the most important issues when dealing with distribution networks. Due to the nature of power loss, it can be an inseparable part of these networks. This is while an optimal reconfiguration is a great optimization procedure to power loss reduction in distribution networks. Moreover, to perform optimal dynamic reconfiguration, determining optimal time intervals and detecting the most proper time points greatly affects the total benefit achieved from this process. This benefit includes the cost of reconfiguration and the benefit of power loss reduction.This paper proposes an evolutionary approach for optimal time interval determination. In this paper basic reconfiguration models are discussed to form an optimal time interval model gradually. The Genetic Algorithm (GA) is used to solve the suggested model while the proposed method is implemented on an IEEE-33 Bus network. In order to examine the effectiveness of the proposed method, a comparison is done with other similar procedures. Also, in order to validate the numerical results, further compression is done with a method using a Binary Particle Swarm Optimization (BPSO) algorithm rather than the GA.

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“…In order to avoid the evaluation of the entire space of the candidate solutions and to minimize the computation burden, several algorithms have been developed. Most authors have used different well known heuristics (branch exchange [2,3,21], branch and bound [1,4], simulated annealing [5]), other heuristic rules or meta-heuristics [7][8][9][11][12][13]15,17,22,23,25,27,28] or multi-agent technologies [20]. On the other hand, some authors have developed methods based on evolutionary computation techniques [6,14,16,18,19,24,26,29,30].…”

Section: Introductionmentioning

confidence: 99%

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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Distribution system reconfiguration for loss reduction: an algorithm based on network partitioning theory

Cited by 75 publications

References 6 publications

Multi-objective distribution feeder reconfiguration by considering energy not supplied with distributed generation

Multi-objective distribution feeder reconfiguration by considering energy not supplied with distributed generation

An evolutionary approach for optimal time interval determination in distribution network reconfiguration under variable load

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

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