Optimal Power Flow of Power Systems Including Distributed Generation Units Using Sunflower Optimization Algorithm

Shaheen, Mohamed A. M.; Hasanien, Hany M.; Mekhamer, S. F.; Talaat, H.

doi:10.1109/access.2019.2933489

Cited by 81 publications

(50 citation statements)

References 45 publications

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“…Additionally, it succeeded in sizing that inherited the optimal allocation of multiple WDGs simultaneously. For a fair comparison, the simulation results obtained by this new proposed strategy using the hybrid CS-PSO technique are compared with the results attained by the sequential ranking buses strategy (Case 2) using the SFO technique [52] for the same conditions. The main comparison results for Cases 2 and 3 are summarised as follows:…”

Section: Figurementioning

confidence: 99%

A novel sizing inherits allocation strategy of renewable distributed generations using crow search combined with particle swarm optimization algorithm

Farh

Eltamaly

Al-Shaalan

et al. 2021

IET Renewable Power Gen

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In this study, a new distributed generation sizing that inherits the allocation strategy for the IEEE 30-bus benchmark system is proposed. The hybrid crow search-particle swarm optimization technique would select the optimal buses through the automatic cancelation of the unfeasible load buses. Additionally, it performs sizing that inherits allocation for minimum costs and transmission losses. Also, it selects the best distributed generation technology among wind turbines and photovoltaic energy systems based on the minimum total capital cost. The proposed strategy outperformed others in terms of total cost alleviation, power losses reduction, voltage profile improvement, and lines loading mitigation.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Figurementioning

confidence: 99%

A novel sizing inherits allocation strategy of renewable distributed generations using crow search combined with particle swarm optimization algorithm

Farh

Eltamaly

Al-Shaalan

et al. 2021

IET Renewable Power Gen

View full text Add to dashboard Cite

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“…The distribution of the persistence model forecast errors is skewed and follows the normal (Gaussian) distribution. Considering it is not easy to sample the wind speed frequency as continuous wind speed, Gaussian regression is used as expressed in (4).…”

Section: Wind Speed and Distribution Of Forecast Errorsmentioning

confidence: 99%

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

et al. 2020

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The complexity of achieving optimal power flow in the presence of renewable resources decreases the accuracy and optimality levels of the power system due to the associated intermittency and uncertainty. The increased challenge of large-scale deployment of wind energy necessitates the proper modeling of wind impact on power system security and reliability levels. This article discusses a new reliable power flow optimization tool that accounts for wind power availability and uncertainty. An accurate wind forecast model is created to maintain power system security considering wind power variability. The error of the forecasting phase is included in the proposed model to accurately predict the available wind power. In this work, the scattered wind data is converted into informative frequency distribution considering the effect of averaging around integers, halves, and quarters. The proposed method maximizes the utilization level of wind energy without deteriorating the system security. The accuracy of the new proposed work is presented by comparing its results with other models discussed in the literature. A complete and integrated formulation of the objective function has been accomplished. The cost function includes transmission losses, generation operating costs, generation gas emissions, and valve-point effects. Reliable and efficient optimization algorithms are adopted to minimize the established cost function of the system ⎯namely, teaching-learningbased optimization and symbiotic organisms search algorithms. The effectiveness of the proposed approach is validated using the IEEE 39-bus system. INDEX TERMS Generation gas emission, generation operating cost, optimal power flow, symbiotic organisms search algorithm, valve-point effects, wind speed forecast.

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“…At this stage, a lot of research work [9][10][11] has been done on the output of distributed generation considering uncertainties, including DG location and capacity, demand side response, network reconfiguration, power and voltage quality, etc. A control algorithm based on improved amplitude adaptive notch filter (AANF) is proposed for generation management of different energy sources in autonomous micro-grid.…”

Section: Introductionmentioning

confidence: 99%

“…A new attempt of utilizing the sunflower optimization (SFO) algorithm in solving the problem of optimal power flow (OPF) in the field of power systems was introduced in order to optimize the generating units' fuel cost under the system constraints. The SFO algorithm is used to minimize the fitness function and yields the best solutions of the problem [11]. A decision-making algorithm that has been developed for the optimum size and placement of DG units in distribution networks.…”

Section: Introductionmentioning

confidence: 99%

A Typical Distributed Generation Scenario Reduction Method Based on an Improved Clustering Algorithm

Guo

et al. 2019

Applied Sciences

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In recent years, distributed generation (DG) technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of distributed generation and to meet the challenges of DG access to the power grid, the multi-scenario analysis method commonly used in DG optimal allocation method is studied in this paper. In order to solve the problems that may arise from using large-scale scenes in the planning of DG considering uncertainties by using multi-scene analysis method, the cluster analysis method suitable for large-scale scene reduction in scene reduction method is introduced firstly, and then an improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the reduction method is verified. Finally, the method mentioned in this paper is compared with other commonly used methods through IEEE-33 node system.

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Optimal Power Flow of Power Systems Including Distributed Generation Units Using Sunflower Optimization Algorithm

Cited by 81 publications

References 45 publications

A novel sizing inherits allocation strategy of renewable distributed generations using crow search combined with particle swarm optimization algorithm

A novel sizing inherits allocation strategy of renewable distributed generations using crow search combined with particle swarm optimization algorithm

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

A Typical Distributed Generation Scenario Reduction Method Based on an Improved Clustering Algorithm

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