Optimal placement of Distributed Generation using combination of PSO and Clonal Algorithm

Sedighizadeh, Mostafa; Fallahnejad, Mostafa; Alemi, Mehdi; Omidvaran, M.; Arzaghi-haris, D.

doi:10.1109/pecon.2010.5697547

Cited by 19 publications

(5 citation statements)

References 13 publications

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“…[23] Another hybrid approach for identifying best location to place DG is combined Clonal algorithm and PSO. [24] The study shows that optimal placement of DG not only helps in reduction of power losses but also enhance the system voltage along with reduction in other factors such as voltage sag, total harmonic distortion etc. which can be harmful for system reliability and operation.…”

Section: 4hybrid Approachesmentioning

confidence: 96%

Optimal Placement of DG for Power Loss Minimization: A Review

Sharma¹,

Nawaz²

2021

SKIT Research Journal

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Distributed generation is the future of electrical energy because the fuels (such as coal) used in conventional methods are depleting day by day. The demand for electricity is increasing rapidly. Hence, consumption of conventional fuels is also increasing resulting in depletion of these resources and high carbon emissions. Also, the harmful impacts caused by these resources has shifted the focus towards more ecofriendly methods i.e. renewable sources of energy. Integrating these renewables with existing system is the new trend. Various studies have been carried out discussing the merits and demerits of DG integration with the central grid. The process of adding distributed generators with the power system is quite challenging. The DG's should be added such that it enhances the system performance and increases reliability. This paper discusses about the optimal placement of DG to reduce power losses in the network.

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Section: 4hybrid Approachesmentioning

confidence: 96%

Optimal Placement of DG for Power Loss Minimization: A Review

Sharma¹,

Nawaz²

2021

SKIT Research Journal

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“…Similarly, uses of different metaheuristic approaches for optimal planning of DG have been implemented in different works such as in refs. [30][31][32][33][34][35][36][37][38][39][40] etc.…”

Section: State-of-the-art In Siting Power Grid Assetsmentioning

confidence: 99%

Siting of small modular reactors with renewable power generation support

Shrestha¹,

Al‐Anbagi

Wagner

2022

IET Renewable Power Gen

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Selecting suitable sites for new distributed generation installations is a critical step for long‐term efficient power generation planning. Locating distributed generations involves studying many factors such as economic, social, environmental, geographic, availability of electrical infrastructure etc. In this paper, a novel system based on the analytical hierarchy process (AHP) and fuzzy AHP (FAHP) are developed to select proper sites for small modular reactor (SMR) power generation units. Renewable energy generation, electrical and non‐electrical loads, existing and retiring generation, transmission lines, and switching stations in location‐dependent scenarios are considered to determine preferable sites. Realistic simulation scenarios are developed to validate the top two locations from the AHP and FAHP SMR siting results using a power system model to show renewable generation and non‐electrical applications support. It is assumed that the surplus power from SMR units is utilised for non‐electrical applications in the selected locations. Moreover, the quality of support for non‐electrical applications are compared with the integration of SMR units to both wind and solar energy.

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“…A combination of genetic algorithm (GA) and particle-swarm-optimisation-(PSO) based methodology for optimal apportion of DG has been presented in Abedini (2012a, 2012b). A conglomeration of PSO and clonal algorithm has been mentioned in Sedighizadeh et al (2010). A mixed integer non-linear optimisation model for DG allocation has been described in Haghighat (2015).…”

Section: Introductionmentioning

confidence: 99%

Plant propagation algorithm for integration of distributed generation in power systems

Mahajan¹,

Vadhera

2018

COMPEL

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Purpose The purpose of this study/paper is to integrate distributed generation optimally in power system using plant propagation algorithm. Distributed generation is a growing concept in the field of electricity generation. It mainly comprises small generation units installed at calculated points of a power system network. The challenge of optimal allocation and sizing of DG is of utmost importance. Design/methodology/approach Plant propagation algorithm and particle swarm optimisation techniques have been implemented where a weighting factor-based multi-objective function is minimised. The objective is to cut down real losses and to improve the voltage profile of the system. Findings The results obtained using plant propagation algorithm technique for IEEE 33-bus systems are compared to those attained using particle swarm optimisation technique. The paper deals with the optimisation of weighting factor-based objective function, which counterpoises the losses and improves the voltage profile of the system and, therefore, helps to deliver the best outcomes. Originality/value This paper fulfils an identified need to study the multi-objective optimisation techniques for integration of distributed generation in the concerned power system network. The paper proposes a novel plant-propagation-algorithm-based technique in appropriate allocation and sizing of distributed generation unit.

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Optimal placement of Distributed Generation using combination of PSO and Clonal Algorithm

Cited by 19 publications

References 13 publications

Optimal Placement of DG for Power Loss Minimization: A Review

Optimal Placement of DG for Power Loss Minimization: A Review

Siting of small modular reactors with renewable power generation support

Plant propagation algorithm for integration of distributed generation in power systems

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