Optimal allocation of capacitors in distribution systems using particle swarm optimization

Singh, S. P.; Rao, Ali Rauf

doi:10.1016/j.ijepes.2012.06.059

Cited by 119 publications

(57 citation statements)

References 45 publications

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“…The presented algorithm MDE have solved the nonlinear optimization problem effectively. In order to further illustrate the advantage of MDE, Particle Swarm Optimization (PSO) [25,26], DE [16], and MDE are used, respectively, to solve Case 4 ten times. In PSO and DE, the population size and maximum number of iterations are all set to 100 and 200.…”

Section: Considering the Reactive Power Support Capabilities Of Wts Amentioning

confidence: 99%

Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

Yuan

Deng

et al. 2016

Energies

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Abstract:In the traditional paradigm, large power plants provide active and reactive power required for the transmission system and the distribution network purchases grid power from it. However, with more and more distributed energy resources (DERs) connected at distribution levels, it is necessary to schedule DERs to meet their demand and participate in the electricity markets at the distribution level in the near future. This paper proposes a comprehensive operational scheduling model to be used in the distribution management system (DMS). The model aims to determine optimal decisions on active elements of the network, distributed generations (DGs), and responsive loads (RLs), seeking to minimize the day-ahead composite economic cost of the distribution network. For more detailed simulation, the composite cost includes the aspects of the operation cost, emission cost, and transmission loss cost of the network. Additionally, the DMS effectively utilizes the reactive power support capabilities of wind and solar power integrated in the distribution, which is usually neglected in previous works. The optimization procedure is formulated as a nonlinear combinatorial problem and solved with a modified differential evolution algorithm. A modified 33-bus distribution network is employed to validate the satisfactory performance of the proposed methodology.

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Section: Considering the Reactive Power Support Capabilities Of Wts Amentioning

confidence: 99%

Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

Yuan

Deng

et al. 2016

Energies

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“…The proposed PSO 2016) approach is tested on IEEE-69 bus distribution system and the result shows that as compared to other heuristic approaches, the proposed algorithm gives smaller DG size. The authors in [35] have used the dynamic sensitivity analysis method for the placement of capacitors which will reduce the search space problem. The results show that for more than one location, dynamic sensitivity is good because it helps in deciding other positions considering the previous locations and the value of capacitor.…”

Section: Optimal Sitingmentioning

confidence: 99%

A Survey on Particle Swarm Optimization for Use in Distributed Generation Placement and Sizing

2016

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Abstract. This paper surveys the research and development of Particle Swarm Optimization (PSO) algorithm for use in selecting a suitable position for Distributed Generation (DG) units within a distribution network. Our discussion first covers the algorithm development of PSO and its use in neural networks. After establishing the foundations of PSO, we then explore its use in sizing and sitting of DG units in distribution network. Combining PSO with other optimization techniques for attaining better results is also discussed in this paper.

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“…On the other hand, a too small max v may trap particles in a local optimum. PSO has been widely applied to various optimization problems and has obtained successful results [e.g., 22,[34][35][36]. For further discussions on PSO, its applications, and related resources, readers can refer to [29,37,38].…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Cost-based Procedure for Multi-response Parameter Design Problems Using GEP, Taguchi Quality Loss, and PSO: Case Study on Heat Sink Design

Hsu¹

2014

International Journal of Computational Intelligence Systems

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Most modern products/processes usually have several quality characteristics that must be optimized simultaneously; this is called a multi-response parameter design problem. To overcome shortcomings in the literature, including insufficient accuracy of second-order polynomials, subjective determination of relative weights and shape coefficients, and non-consideration of manufacturing or material costs, this paper proposes a cost-based procedure for resolving multi-response parameter design problems using gene expression programming (GEP), Taguchi quality loss, and particle swarm optimization (PSO). A case study with the aim of optimizing the design of a heat sink applied to a high-power MR16 LED lamp was used to demonstrate the proposed procedure. The experimental results indicated that the proposed procedure can provide highly robust settings for design parameters that can maximize the thermal performance and minimize the actual material cost of a heat sink. Furthermore, decisionmakers no longer need to subjectively determine the relative weight of each response. Therefore, the proposed approach can be considered to be feasible and effective; it has the potential to be a useful tool for resolving general multi-response parameter design problems in the real world.

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Optimal allocation of capacitors in distribution systems using particle swarm optimization

Cited by 119 publications

References 45 publications

Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation

A Survey on Particle Swarm Optimization for Use in Distributed Generation Placement and Sizing

Cost-based Procedure for Multi-response Parameter Design Problems Using GEP, Taguchi Quality Loss, and PSO: Case Study on Heat Sink Design

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