Optimal Distributed Generation Parameters for Reducing Losses with Economic Consideration

Le, A.D.T.; Kashem, Mohammad Abul; Negnevitsky, Michael; Ledwich, Gerard

doi:10.1109/pes.2007.386058

Cited by 52 publications

(19 citation statements)

References 20 publications

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“…The above mentioned impacts are applied in several optimisation programs to determine an optimal size and siting of a DG unit [2,6,13,14,17]. The main target of these programs is to minimize the network losses while the cost of the DG unit is kept as low as possible.…”

Section: Impact Of Dg On the Distribution Network -An Overviewmentioning

confidence: 99%

“…The injections of power by the distributed generators may change the magnitude and even the direction of the power flow in the network. This has several implications for the operation and planning of the network and has several technical and economic consequences [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In this framework it is clear that DG units affect the network losses, which are defined as the difference between the energy sent out from the generating stations and the energy metered at the customer premises.…”

Section: Introductionmentioning

confidence: 99%

“…The impacts of DG on the network are given in the next section, with a special emphasis on the influence of DG on the network losses [11,15,16]. The calculation of these network losses are applied in several optimisation programs to quantify an optimal size and location of the DG unit [2,6,13,14,17]. In these programs the power quality can be taken into account.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of converter - connected distributed generation on distribution network losses

Degroote

Renders²,

Meersman³

et al. 2024

RE&PQJ

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Distributed generation (DG) is predicted to play an increasing role in the electric power system of the near future. There are different types of DG units from the constructional and technological points of view, which have a different influence on the distribution network. The impact of DG on the network is considered, with a more extensive discussion on the influence of DG on the network losses. The calculation of these network losses is applied in several optimisation programs to quantify an optimal size and location of the DG unit. In these programs the power quality can be taken into account but harmonic losses are rarely considered, although it is known that several DG topologies can have a negative influence on the total harmonic distortion and thus on the harmonic losses. Therefore, this paper will investigate the influence of converter-connected DG units on fundamental and harmonic losses in the distribution network.

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Section: Impact Of Dg On the Distribution Network -An Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of converter - connected distributed generation on distribution network losses

Degroote

Renders²,

Meersman³

et al. 2024

RE&PQJ

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show abstract

“…This increases the free capacity of lines and therefore prevents from establishment of new lines, substations and etc. [1][2][3][4][26][27][28][29][30][31][32][33] With calculating the current flows through the lines, before and after the installation of DGs and comparing thenetworkflow in these two states, another component of the objective function will be built.…”

Section: Index Ofline's Free Capacitymentioning

confidence: 99%

Applying BCO Algorithm to Solve the Optimal DG Placement and Sizing Problem

Sohi¹,

Shirdel²

2012

EEE

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In this paper, the problem of DG placement and sizing for loss reduction and line capacity improvement has been considered and evaluated. In order to solve the optimization problem, authors are using the novel and heuristic BCO algorithm. This algorithm is inspired of the intelligent behaviour of bees during the nectar search process. Finally, the results of applying the Bee Colony Optimization algorithm for DG placement and sizing in IEEE 33 nodes test network are shown.

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“…Other researchers like Le et al and Durga et al point this problem from different views. The first group discusses the matter of DG size and allocation taking economic aspects into consideration using sequential quadratic programming (SQP) algorithm [7]. Meanwhile, Durga and Mithulananthan consider the DG existence in the corridor of deregulated electricity market [8].…”

Section: Introductionmentioning

confidence: 99%

Multi-Deployment of Dispersed Power Sources Using RBF Neural Network

Qudaih¹,

Hiyama²

2010

EPE

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Multi-deployment of dispersed power sources became an important need with the rapid increase of the Distributed generation (DG) technology and smart grid applications. This paper proposes a computational tool to assess the optimal DG size and deployment for more than one unit, taking the minimum losses and voltage profile as objective functions. A technique called radial basis function (RBF) neural network has been utilized for such target. The method is only depending on the training process; so it is simple in terms of algorithm and structure and it has fast computational speed and high accuracy; therefore it is flexible and reliable to be tested in different target scenarios. The proposed method is designed to find the best solution of multi-DG sizing and deployment in 33-bus IEEE distribution system and create the suitable topology of the system in the presence of DG. Some important results for DG deployment and discussion are involved to show the effectiveness of our proposed method.

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Optimal Distributed Generation Parameters for Reducing Losses with Economic Consideration

Cited by 52 publications

References 20 publications

Influence of converter - connected distributed generation on distribution network losses

Influence of converter - connected distributed generation on distribution network losses

Applying BCO Algorithm to Solve the Optimal DG Placement and Sizing Problem

Multi-Deployment of Dispersed Power Sources Using RBF Neural Network

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