An improved gravitational search algorithm for optimal placement and sizing of renewable distributed generation units in a distribution system for power quality enhancement

Kadir, Aida Fazliana Abdul; Mohamed, Azah; Shareef, Hussain; Ibrahim, Ahmad Asrul; Khatib, Tamer; Elmenreich, Wilfried

doi:10.1063/1.4878997

Cited by 20 publications

(6 citation statements)

References 38 publications

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“…An artificial bee colony (ABC) based method was presented in [20] to solve the OPDG problem by minimizing the power loss. Several algorithms were also developed to solve that problem, such as the modified honey bee mating optimization algorithm (HBMO) [21], the improved gravitational search algorithm (IGSA) [22], and symbiotic organisms search (SOS) algorithm [23].…”

Section: Introductionmentioning

confidence: 99%

A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems

et al. 2019

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In this paper, a novel, combined evolutionary algorithm for solving the optimal planning of distributed generators (OPDG) problem in radial distribution systems (RDSs) is proposed. This algorithm is developed by uniquely combining the original differential evolution algorithm (DE) with the search mechanism of Lévy flights (LF). Furthermore, the quasi-opposition based learning concept (QOBL) is applied to generate the initial population of the combined DELF. As a result, the new algorithm called the quasi-oppositional differential evolution Lévy flights algorithm (QODELFA) is presented. The proposed technique is utilized to solve the OPDG problem in RDSs by taking three objective functions (OFs) under consideration. Those OFs are the active power loss minimization, the voltage profile improvement, and the voltage stability enhancement. Different combinations of those three OFs are considered while satisfying several operational constraints. The robustness of the proposed QODELFA is tested and verified on the IEEE 33-bus, 69-bus, and 118-bus systems and the results are compared to other existing methods in the literature. The conducted comparisons show that the proposed algorithm outperforms many previous available methods and it is highly recommended as a robust and efficient technique for solving the OPDG problem.

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Section: Introductionmentioning

confidence: 99%

A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems

et al. 2019

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“…Based on Newton's equations of motion and gravity, it is a brand-new meta-heuristic optimization method [26]. The steps listed below can be used to describe the GSA [27,28].…”

Section: Gravitational Search Algorithmmentioning

confidence: 99%

Optimal Placement and Sizing of DSTATCOM in Radial Distribution System Using Gravitational Search Algorithm

2023

5th ISSE National Conference (INAC-05) on Systems Approach for Self-Reliance in Advanced Technologies

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In order to reduce power losses and improve the voltage profile of the Radial Distribution network, a Gravitational Search Algorithm (GSA) is described in this work. A backward forward load flow algorithm is taken as a base case and Loss Sensitivity Factors (LSF) are used for identifying the candidate buses for D-STATCOM placement whereas the optical allocation and sizing of D-STATCOM is done by using GSA. The proposed work has been evaluated using the industry-standard IEEE 33-Bus Radial Distribution Test network. The acquired results have been compared with the results obtained in Base Case, Genetic Approach (GA), Immune Algorithm (IA), Harmony Search Algorithm (HSA), Power Loss Index (PLI) technique, and Differential Evaluation Algorithm in order to verify the usefulness of the proposed algorithm (DEA). MATLAB software was used to run the simulation.

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“…The optimal location and size of RDGs have been the subject of numerous studies. Researchers have focused on developing methodologies that minimize total power losses while determining optimal locations and sizes [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22] and improve voltage profiles [10], [13], [15], [17], [21]. In [9], the authors proposed an analytical expression to estimate the optimal size of the distributed generator (DG) and presented a methodology to determine their locations.…”

Section: Introductionmentioning

confidence: 99%

Optimal Location and Sizing of Renewable Distributed Generators for Improving Robust Voltage Stability Against Uncontrollable Reactive Compensation

et al. 2023

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The penetration level of renewable energy sources is increasing worldwide with incentives and subsidies for declining greenhouse gas emissions. Nevertheless, determining the optimal location and size of renewable distributed generators (RDGs) remains a challenging task, owing to the uncontrollable reactance that dominates power distribution networks in voltage control and its sensitivity to weather conditions. Hence, without considering the reactive compensation of generators, RDG integration incurs undesired total power losses and puts the system at risk for voltage instability and collapse. This research proposes Load Disabling Nodal Analysis for Robust Voltage Stability (LDNA-RVS), a method that determines the optimal location and size of RDGs and aims to improve robust voltage stability by considering reactive compensation while enhancing the loss reduction efficiency (LRE) of the RDG integration. The proposed LDNA-RVS method has been successfully applied to the IEEE 33-bus and IEEE 69-bus test distribution systems, demonstrating its suitability for small-scale systems with a limited number of RDGs. Finally, LDNA-RVS outperforms other methods in six out of eight categories for robust voltage stability and achieves the top rank in all eight categories for LRE. These findings prove the effectiveness of LDNA-RVS in terms of robust voltage stability and LRE against the uncontrollable reactive compensation.

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An improved gravitational search algorithm for optimal placement and sizing of renewable distributed generation units in a distribution system for power quality enhancement

Cited by 20 publications

References 38 publications

A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems

A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems

Optimal Placement and Sizing of DSTATCOM in Radial Distribution System Using Gravitational Search Algorithm

Optimal Location and Sizing of Renewable Distributed Generators for Improving Robust Voltage Stability Against Uncontrollable Reactive Compensation

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