Reconfiguration of distribution systems for loss reduction using the hyper-cube ant colony optimisation algorithm

Abdelaziz, Almoataz Y.; Osama, Reham A.; Elkhodary, Salem M.

doi:10.1049/iet-gtd.2011.0281

Cited by 57 publications

(20 citation statements)

References 37 publications

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“…These methods find admirable solutions for the medium size systems and are not suitable for large systems [6]. In recent years, new heuristic optimization algorithms like Genetic Algorithm (GA) [7 ÷ 11], Nondominated Sorting Genetic Algorithms (NSGA) [12], matroid theory [13], other meta-heuristics techniques like plant growth [14], Particle Swarm Optimization (PSO) [15], tabu search [16] and ant colony search [17,18] have been proposed for DSR problem. They are aimed to deal with large system with fast execution time [19].…”

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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“…Abdelaziz et al introduced the Hyper-Cube (HC) framework to the ACO algorithm for solving the reconfiguration problem of distribution systems [91]. The HC-ACO uses the transition states for updating the amount of pheromones at each iteration, maintaining the maximum value of pheromone as unity while that branch is part of the solution at every iteration.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Quintero-Duran

Candelo-Becerra

Sousa

2017

JESTR

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Distribution network reconfiguration (DNR) continues to be a good option to reduce technical losses in a distribution power grid. However, this non-linear combinatorial problem is not easy to assess by exact methods when solving for large distribution networks, which requires large computational times. For solving this type of problem, some researchers prefer to use metaheuristic techniques due to convergence speed, near-optimal solutions, and simple programming. Some literature reviews specialize in topics concerning the optimization of power network reconfiguration and try to cover most techniques. Nevertheless, this does not allow detailing properly the use of each technique, which is important to identify the trend. The contributions of this paper are three-fold. First, it presents the objective functions and constraints used in DNR with the most used metaheuristics. Second, it reviews the most important techniques such as particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO), immune algorithms (IA), and tabu search (TS). Finally, this paper presents the trend of each technique from 2011 to 2016. This paper will be useful for researchers interested in knowing the advances of recent approaches in these metaheuristics applied to DNR in order to continue developing new best algorithms and improving solutions for the topic.

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“…The same type customers have the same load curve, and the maximum load of each load point in one year is refereed in [15]. In dynamic case, the load data at any time can be generated by the terms of (8)- (9) according to the load type of k and the time t .…”

Section: B Time-varying Load Demand Casementioning

confidence: 99%

“…To find an appropriate solution for the static case, meta-heuristic methods are frequently used and they have been clearly demonstrated to be both feasible and advantageous [1][2][3][4][5][6][7][8][9][10][11] . This review is focused on some contributions related to GAs, as GA or enhanced GA can efficiently identify the optimal or near optimal network configurations.…”

Section: Introductionmentioning

confidence: 99%

Multi-stage Reconfiguration of Distribution Network for Loss Reduction and Reliability Improvement

Zhang¹,

Duan²

2015

Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Abstract-Electrical distribution network reconfiguration is a complex combinatorial optimization process aimed at finding a radial operating structure that minimizes the system power loss or maximizes the system reliability while satisfying operating constraints. It has been traditionally performed assuming that loads of buses are constant values, but in real systems the values always change with time. To ensure the system operate safely and economically, decision makers have to reconfigure the distribution feeder dynamically with time according to the changing loads. In this paper, a multi-stage reconfiguration method is presented for both the indices of power loss reduction and reliability improvement with a load curve model. An enhanced genetic optimization algorithm is used to handle the reconfiguration problem so as to determine the number of stages and switch operation schemes in each stage. The effectiveness of the proposed method is demonstrated on 33-bus radial distribution system and a practical system.

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Reconfiguration of distribution systems for loss reduction using the hyper-cube ant colony optimisation algorithm

Cited by 57 publications

References 37 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

Recent Trends of the Most Used Metaheuristic Techniques for Distribution Network Reconfiguration

Multi-stage Reconfiguration of Distribution Network for Loss Reduction and Reliability Improvement

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