Hybrid GA and OPF evaluation of network capacity for distributed generation connections

Harrison, Gareth; Piccolo, Antonio; Siano, Pierluigi; Wallace, Alfred Russel

doi:10.1016/j.epsr.2007.03.008

Cited by 180 publications

(90 citation statements)

References 20 publications

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“…GA was also successfully used in noteworthy papers by Biswas et al [1], El-Ela et al [6], López-Lezama et al [15] and Harrison et al [16].…”

Section: Optimal Solution Findingmentioning

confidence: 99%

Untitled

2016

Teh. vjesn.

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Original scientific paper The paper analyzes the possibility of reducing active power losses in power system, constrained by regulated voltage levels, by implementing appropriate distributed generation capacity. The objectives of this paper were achieved by developing hybrid methods based on artificial neural network and genetic algorithm. Methods have been developed to determine the impact of different distributed generation power on all terminals in the observed system. The method that uses artificial neural network and genetic algorithm is applicable for radial distribution networks, and method using load flow and genetic algorithm is applicable to doubly-fed distribution network. For comparison purposes, additional method was developed that uses neural networks for the decision-making process. Data for training the neural network was obtained by power flow calculation in the DIgSILENT PowerFactory software on a part of Croatian distribution network. The same software was used as an analytical tool for checking the correctness of solutions obtained by optimization. Keywords: artificial neural networks; distributed generation; genetic algorithm Kontrola naponskih prilika i gubitaka snage korištenjem distribuirane proizvodnje i računalne inteligencijeIzvorni znanstveni članak U radu se analizira mogućnost smanjenja aktivnih gubitaka elektroenergetskog sustava, uz poštivanje propisanih naponskih razina, primjenom odgovarajućih kapaciteta distribuirane proizvodnje. Ciljevi ovog rada ostvareni su razvojem hibridnih metoda baziranih na umjetnim neuronskim mrežama i genetskom algoritmu. Razvijene su metode za određivanje utjecaja distribuirane proizvodnje različitih snaga na svim čvorovima u promatranom sustavu. Metoda koja koristi umjetnu neuronsku mrežu i genetski algoritam primjenjiva je za radijalne distributivne mreže, a metoda koja koristi proračun tokova snaga i genetski algoritam primjenjiva je za dvostruko napajane distributivne mreže. S ciljem usporedbe razvijena je i metoda koja koristi neuronske mreže za proces odlučivanja o najboljem rješenju. Podaci za učenje neuronske mreže dobiveni su proračunom tokova snaga u programskom alatu DIgSILENT PowerFactory i to na djelu hrvatske distributivne mreže. Isti programski alat se koristi analitički kao sredstvo provjere ispravnosti rješenja dobivenih optimizacijom.

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“…GA was also successfully used in noteworthy papers by Biswas et al [1], El-Ela et al [6], López-Lezama et al [15] and Harrison et al [16].…”

Section: Optimal Solution Findingmentioning

confidence: 99%

Untitled

2016

Teh. vjesn.

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“…One of the appropriate models reported in the literature is calculating the difference between total loss of the system before and after DG placement [1], [5], [14], [16], [18].…”

Section: ) Total Incentive For Active Loss Reductionmentioning

confidence: 99%

“…In some models [16], the DNO should pay/receive equal to the electricity price multiplied by amount of loss reduction/increase and in some models [1], [5], [14], [18] a fix incentive, i.e. ψ, is paid to DNO for each MWh reduction of active losses .…”

Section: ) Total Incentive For Active Loss Reductionmentioning

confidence: 99%

“…In [19], an ordinal optimization approach for reducing the search space of the proposed problem in [18] which shows improvement in the results. In this paper, a heuristic method named Immune-GA (IGA) is proposed to find the optimal size and placement of DG units in an unbundled DNO environment.…”

Section: Introductionmentioning

confidence: 99%

“…The reference [6], proposes a method based on Kalman Filter algorithm to find the optimal size of DG units to reduce active losses the location of these units are found by clustering the buses in networks into the number of DG units and selecting the largest bus in each cluster as the best candidate bus for DG installation. In [18], a hybrid GA-OPF is proposed to find the place and also the capacity of a predefined number of DG units to increase the incentives received by DNO due to network reinforcement deferral and loss reduction. In this model, the mixed integer non-linear problem is divided into two parts.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient immune‐GA method for DNOs in sizing and placement of distributed generation units

Soroudi

Ehsan

2010

Int Trans Elec Energy Syst

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This paper proposes a hybrid heuristic optimization method based on genetic algorithm and immune systems to maximize the benefits of distribution network operators (DNOs) accrued due to sizing and placement of distributed generation (DG) units in distribution networks. The effects of DG units in reducing the reinforcement costs and active power losses of distribution network have been investigated. In the presented method, the integration of DG units in distribution network is done considering both technical and economical aspects. The strength of the proposed method is evaluated by applying it on a small and a realistic large scale distribution network and the results are compared with analytical classic and other heuristic methods and discussed.

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