A New Method for Placement of DG Units in Distribution Networks

Hedayati, H.; Nabavi-Niaki, S.A.; Akbarimajd, Adel

doi:10.1109/psce.2006.296204

Cited by 19 publications

(7 citation statements)

References 21 publications

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“…The analysis will be conducted in IEEE 30-bus test system, hence there are thirty locations to be determined for DG placement. The overall optimization problem can be summarized as follows: (8) ΣP G = ΣP D + P loss (9) ΣQ G = ΣQ D + Q loss (10) Control variables in the FA are : i-th individual in Firefly Algorithm Constraint (6) is significant for overall system stability assessment, while constraint (7) ensures that the voltage does not violate the system requirement. In this study, the lower limit for the voltage is set to be 0.85 per-unit rather than 0.95 per-unit due to the increasing reactive loading that will be used in the experiment.…”

Section: Proposed Problem Formulationmentioning

confidence: 99%

“…The optimal installation, sizing and operation of DG units connected directly to the network can be determined by several methods. Mainly, there are four methods can be used in optimal DG placement such as the L-index method, Voltage Stability Margin Index (VSMI) method, Voltage Performance Index (VPI) method and Loss Sensitivity Factor (LSF) [6][7][8][9][10]. Placement via LSF requires simulation of load flow to obtain the resulted real power loss with respect to the DG's injected real power (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Optimal sizing of distributed generation using firefly algorithm and loss sensitivity for voltage stability improvement

Hamid

Jipinus

Musirin

et al. 2020

IJEECS

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This paper proposes an optimization technique for distributed generation (DG) sizing in power system. The DG placement was done through Loss Sensitive (LS) technique to determine the suitable locations. The LS index is calculated such that the change in power losses is divided with generation increment and a rank of buses is obtained to identify the suitable locations for DG placement. Subsequently, a meta-heuristic algorithm, known as Firefly Algorithm (FA) was run to obtain the optimal size or capacity of the DG. The installation takes into consideration the aspect of voltage stability in terms of total real power losses and voltage profiles to be improved in the distribution system. Based on the experiment, the real power losses and voltage profiles were improved significantly as a result of the DG placement. In addition, the installation could prevent the power system from collapse as the reactive loading was increased to maximum.

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Section: Proposed Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal sizing of distributed generation using firefly algorithm and loss sensitivity for voltage stability improvement

Hamid

Jipinus

Musirin

et al. 2020

IJEECS

View full text Add to dashboard Cite

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“…At each generation of the EA, a new set of approximations is created by the process of selecting individuals according to their fitness in the problem domain and reproducing them using variation operators [8]. Reference [9] presented a technique to determine optimal location and sizing of DG to increase maximum loadability limits. The computation of maximum loadability is crucial since it is capable to measure how close the system operation to its stability limit.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective quantum-inspired evolutionary programming for optimal location and sizing of distributed generation

Yasin

Rahman

Zakaria

2012

2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT)

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This paper presents Quantum-Inspired Evolutionary Programming (QIEP) technique to determine the optimal locations and sizing of multiple distributed generations (DG) in a distribution system. QIEP is an algorithm that employs the concept of quantum mechanics in the Evolutionary Programming (EP). Quantum-Inspired is implemented according to three levels defined by quantum individuals, quantum groups and quantum universe. The problem formulation is based on a multiobjective model in which the multiobjective are defined as reducing power losses, increasing maximum load ability limits and cost minimisation. Three cases are considered to test the effectiveness of the proposed technique namely single objective function, multiobjective function with fixed weighted sum and multiobjective function with randomly optimised weighted sum. The performances of the multiobjective QIEP optimisation technique were compared with those obtain from conventional evolutionary programming in terms of fitness values and computation time.The proposed study was conducted on the IEEE 69-bus distribution test system and the 141-bus distribution system.

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“…Optimal distributed generation (DG) placement, a mixed integer nonlinear programming problem, has been solved by various methods including repetitive load flow [1], the weakest bus searching [2], traditional basic particle swarm (BPSO) [3], genetic algorithm (GA) [4], and linear programming [5]. Moreover, there are various objectives for optimal DG placement.…”

Section: Introductionmentioning

confidence: 99%

“…GA in Ref. [4] optimized the allocation and sizing of DGs to trade off between the benefit of loss reduction cost and total cost including investment cost, installation cost, and maintenance cost. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Optimal multiple distributed generation placement in microgrid system by improved reinitialized social structures particle swarm optimization

Prommee

Ongsakul

2010

Euro. Trans. Electr. Power

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SUMMARYThis paper proposes an improved reinitialized social structures particle swarm optimization (IRS-PSO) for solving optimal multiple distributed generations (DG) placement in a microgrid (MG) system. The movement of each particle in IRS-PSO is pulled by an inertia term, a cognitive term (personal best), and three social learning terms including global best, local best, and near neighbor best. The objective is to minimize the real power loss within real and reactive power generation limits and voltage limits. Five types in a MG system are considered including MG with DG supplying real power only, MG with DG supplying reactive power only, MG with DG supplying real power and consuming reactive power, MG with DG supplying real power and reactive power, and MG with four different types of DG regulating the bus voltage. For a given number of DG units in each type, IRS-PSO can find better sizes and locations of multiple DGs than repetitive load flow, basic particle swarm optimization (BPSO), adaptive weight particle swarm optimization (APSO), and global best, local and near neighbor best particle swarm optimization (GLN-PSO) on the 69-bus radial MG distribution system.

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A New Method for Placement of DG Units in Distribution Networks

Cited by 19 publications

References 21 publications

Optimal sizing of distributed generation using firefly algorithm and loss sensitivity for voltage stability improvement

Optimal sizing of distributed generation using firefly algorithm and loss sensitivity for voltage stability improvement

Multiobjective quantum-inspired evolutionary programming for optimal location and sizing of distributed generation

Optimal multiple distributed generation placement in microgrid system by improved reinitialized social structures particle swarm optimization

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