Optimal multi-distributed generation placement by adaptive weight particle swarm optimization

Prommee, W.; Ongsakul, Weerakorn

doi:10.1109/iccas.2008.4694499

Cited by 49 publications

(26 citation statements)

References 3 publications

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“…Most of them find the optimal allocation and size of single DG in order to reduce losses and improve voltage profiles [6,[9][10][11][12][13]. Others include the placement of multiple DGs with artificial intelligence-based optimization methods [7,[14][15][16][17][18] and a few go with analytical approach [11,17].…”

Section: Introductionmentioning

confidence: 99%

Hybrid approach for optimal placement of multiple DGs of multiple types in distribution networks

Kansal¹,

Kumar

Tyagi

2016

International Journal of Electrical Power & Energy Systems

239

117

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Section: Introductionmentioning

confidence: 99%

Hybrid approach for optimal placement of multiple DGs of multiple types in distribution networks

Kansal¹,

Kumar

Tyagi

2016

International Journal of Electrical Power & Energy Systems

239

117

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“…In this paper, for real power loss minimization the solar photovoltaic (SPV) and wind energy based renewable DGs are considered for integration with capacitor banks in RDN. Based on their real and reactive power generation capacity they are modeled as follows [7]- [9] (Table 1). …”

Section: Introductionmentioning

confidence: 99%

Optimum Simultaneous Allocation of Renewable Energy DG and Capacitor Banks in Radial Distribution Network

Rajeswaran¹,

Kamaraj²

2016

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Nowadays the optimal allocation of distributed generation (DG) in the distribution network becomes the popular research area in restructuring of power system. The capacitor banks introduced in the distribution networks for reactive power compensation also have the capacity to minimize the real and reactive power losses occurred in the system. Hence, this research integrates the allocation of renewable energy DG and capacitor banks in the radial distribution network to minimize the real power loss occurred in the system. A two-stage methodology is used for simultaneous allocation of renewable DG and capacitor banks. The optimum location of renewable energy DG and capacitor banks is determined using the distributed generation sitting index (DGSI) ranking method and the optimum sizing of DG and capacitor banks is found out for simultaneous placement using weight improved particle swarm optimization algorithm (WIPSO) and self adaptive differential evolution algorithm (SADE). This two-stage methodology reduces the burden of SADE and WIPSO algorithm, by using the DGSI index in determining the optimal location. Hence the computational time gets reduced which makes them suitable for online applications. By using the above methodology, a comprehensive performance analysis is done on IEEE 33 bus and 69 bus RDNs and the results are discussed in detail.

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“…If wind turbine is connected to an induction generator, Wind generator supply real power and in turn absorbs reactive power. When a wind generator of size P DG is placed at bus j the absorbing reactive power can be given as [13] : …”

Section: Casementioning

confidence: 99%

“…The particle updates its velocity according to its previous velocity and the distances to its current position from both its own best historical position and the best positions of the neighbors in every iteration step, and then it flies towards a new position given by [12,13] .…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Optimal location and capacity of multi-distributed generation for loss reduction and voltage profile improvement using imperialist competitive algorithm

Rostamzadeh¹,

Valipour²,

Shenava³

et al. 2012

AIR

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This paper proposes an Imperialist Competitive Algorithm (ICA) for optimal multiple distributed generations (DGs) placement and sizing in a distribution system. The objective is to minimize the total real power losses and improve the voltage profile within real and reactive power generation and voltage limits. Three types of DG are considered and the ICA is used to find the better sizes and locations of DGs for maximum real power losses reduction and voltage improvement for given number of DG units in each type. Both integer and continuous variables are considered in ICA, integer variable for locations and continues variable for sizes. The total real power losses and voltage profile evaluation are based on a power flow method for radial distribution system with the representation of DGs. The proposed method has been demonstrated on 33 bus radial distribution system. The efficiency of the ICA in reducing the total power losses and improving voltage is validated by comparing the obtained results with Particle Swarm Optimization (PSO) algorithm.

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Optimal multi-distributed generation placement by adaptive weight particle swarm optimization

Cited by 49 publications

References 3 publications

Hybrid approach for optimal placement of multiple DGs of multiple types in distribution networks

Hybrid approach for optimal placement of multiple DGs of multiple types in distribution networks

Optimum Simultaneous Allocation of Renewable Energy DG and Capacitor Banks in Radial Distribution Network

Optimal location and capacity of multi-distributed generation for loss reduction and voltage profile improvement using imperialist competitive algorithm

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