Optimal Placement of DG in Distribution System Using Genetic Algorithm

Sattianadan, D.; Sudhakaran, M.; Dash, Subhransu Sekhar; Vijayakumar, K.; Ravindran, P.

doi:10.1007/978-3-319-03756-1_57

Cited by 16 publications

(12 citation statements)

References 14 publications

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“…The output power of PV module, operating at maximum power point at solar irradiance s, may be estimated using Equation (16).…”

Section: Practical Sizing Of Pv Dgmentioning

confidence: 99%

“…Ayodele et al [15] used GA to find the best DG technology for optimal power system functioning, as well as the best position and size of the DG to reduce network power loss. GA is applied to reduce the cost of system expansion and improves system stability [16,17]. However, GA convergence time is high, especially, when applied in the solution of complex problems, and may suggest inaccurate solutions.…”

Section: Introductionmentioning

confidence: 99%

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Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

et al. 2021

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The restructuring of power systems and the ever-increasing demand for electricity have given rise to congestion in power networks. The use of distributed generators (DGs) may play a significant role in tackling such issues. DGs may be integrated with electrical power networks to regulate the drift of power in the transmission lines, thereby increasing the power transfer capabilities of lines and improving the overall performance of electrical networks. In this article, an effective method based on the Harris hawks optimization (HHO) algorithm is used to select the optimum capacity, number, and site of solar-based DGs to reduce real power losses and voltage deviation. The proposed HHO has been tested with a complex benchmark function then applied to the IEEE 33 and IEEE 69 bus radial distribution systems. The single and multiple solar-based DGs are optimized for the optimum size and site with a unity power factor. It is observed that the overall performance of the systems is enhanced when additional DGs are installed. Moreover, considering the stochastic and sporadic nature of solar irradiance, the practical size of DG has been suggested based on analysis that may be adopted while designing the actual photovoltaic (PV) plant for usage. The obtained simulation outcomes are compared with the latest state-of-the-art literature and suggest that the proposed HHO is capable of processing complex high dimensional benchmark functions and has capability to handle problems pertaining to electrical distribution in an effective manner.

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“…The output power of PV module, operating at maximum power point at solar irradiance s, may be estimated using Equation (16).…”

Section: Practical Sizing Of Pv Dgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

et al. 2021

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“…The results show that, compared to the proposed method, the genetic algorithm finds the optimal problem at lower speeds. In [13], the locating of dispersed generation in distribution networks is provided by considering the voltage stability criteria. In this study, the genetic algorithm is used to find the best answer.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective TLBO and GWO-based Optimization for Placement of Renewable Energy Resources in Distribution System

KC¹,

Alkhwaildi²

2021

CRPASE

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The use of renewable solar and wind resources as distributed generation sources in distribution networks has been welcomed by network operators. In order to exploit the maximum benefits of using these distributed products, the location of installation and their capacity should be determined optimally in the distribution network. In this paper, in order to optimize the placement of solar panels and wind turbines in the distribution network with the aim of reducing losses and improving reliability based on Energy Not Supplied subscribers (ENS), a multi-objective evolutionary algorithm based on fuzzy decision method, called the Multi-Objective Hybrid Training Learning Based Optimization-Grey Wolf Optimizer (MOHTLBOGWO) proposed that has a High optimization speed and not trapped at all in the optimal local. At first, the candidate buses are set for the installation of renewable resources using the Loss Sensitivity Factor (LSF). Then the proposed method is used to determine the location and optimal capacity of renewable resources through the candidate bases. Proposed issues have been implemented in a single-objective and multiobjective manner on a 33 bus IEEE radial distribution network. Also, in this paper, the effect of distributing renewable resources on the characteristics of the distribution network is evaluated. The results obtained from the proposed algorithm are compared with the results of other algorithms to demonstrate the superiority of the proposed method in reducing losses, improving reliability, and increasing the financial profit of the network. Simulation results show the better performance of the proposed method in comparison with Teaching-Learning Based Optimization (TLBO) and Grey Wolf Optimiser (GWO) methods and past studies to achieve optimal results. Also, the results show that distributing of the capacity and location of distributed renewable generation leads to a further reduction in losses and a better improvement of the reliability criterion.

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“…In references [7,11] and [12] have formulated MINLP models to locate and dimension DG and their solutions have been obtained using GAMS, the main advantage of these implementations is that the authors have concentrated their efforts to obtain accurate optimization models for representing the optimization problem; however, the GAMS package has a high probability of reporting local solutions due to the non-convex nature of the exact MINLP model. Regarding the combinatorial optimization, i.e., metaheuristics, in literature can be found multiple approaches to solve the optimal location and sizing DG, some of them are: krill-herd optimization algorithm [1,13]; genetic algorithms [14,15,16,17]; particle swarm optimization [3,18,19]; sunflower optimization algorithm [20]; population-based incremental learning [8]; tabu search algorithm [21,22]; and flower pollination algorithm [23,24], among others.…”

Section: Introductionmentioning

confidence: 99%

Black hole optimizer for the optimal power injection in distribution networks using DG

Montoya

Ramı́rez

Grisales-Noreña

2021

J. Phys.: Conf. Ser.

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The optimal sizing of Distributed Generators (DG) in electric power distribution networks is carried out through a metaheuristic optimization strategy. To size DG it is proposed an optimal power flow model is formulated by considering that the location of these sources has been previously defined by the distribution company. The solution of the optimal power flow is reached with the Black Hole Optimizer (BHO). A methodology is used master-slave optimization methodology, where the BHO (i.e., master stage) defines the sizes of the DG and the slave stage evaluates the objective function with a load flow algorithm, this work using the triangular-based power flow method. Numerical results in the 33-node and the 69-node test system demonstrates the effectiveness and robustness of the proposed approach when compared with literature results.

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Optimal Placement of DG in Distribution System Using Genetic Algorithm

Cited by 16 publications

References 14 publications

Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

Multi-Objective TLBO and GWO-based Optimization for Placement of Renewable Energy Resources in Distribution System

Black hole optimizer for the optimal power injection in distribution networks using DG

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