A cloud model based symbiotic organism search algorithm for DG allocation in radial distribution network

Kawambwa, Shamte; Ndyetabura, Hamisi; Mafole, Prosper; Kundaeli, Helard

doi:10.1007/s12065-020-00529-y

Cited by 16 publications

(6 citation statements)

References 44 publications

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“…The VSI is particularly useful for distribution networks, where maintaining voltage stability is crucial. The VSI is typically calculated based on the voltage deviation (VD) from the nominal value and the power injection at a specific bus [8]. The following are the general steps to calculate VSI along with the mathematical equations:…”

Section: Concept Of Voltage Stability Index (Vsi)mentioning

confidence: 99%

“…The problem of DG allocation on RDS has been the subject of extensive investigation. However, careful consideration must be given to the device's optimal placement in order to maximize the technical, financial, and environmental benefits; failing to do so can result in voltage instability, increased system losses, and reverse power flow, all of which pose risks to the operation of the entire system [8]. Therefore, the system's potential benefits are significantly impacted by the device's ideal placement [9].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Allocation of Distributed Generation Using Improved Cuckoo Search Algorithm

Timta,

Umar,

Timta

et al. 2024

PakJET

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This work presents an improved cuckoo search algorithm based on Success Rate (CSA-SR) for optimal allocation of Distributed Generation (DG). This is necessary in order to improve the power quality of the distribution system by minimizing power loss and improving voltage stability. Voltage Stability Index (VSI) is applied to identify restricted unhealthy buses/areas in the distribution networks. The work is implemented on standard IEEE 33 Bus test systems. Newton Raphson Power Flow (NRPF) technique is used in MATLAB R2019b environment for the power flow analysis. The overall IEEE 33 Bus system real power loss obtained from the simulation result is found to be 202.7071MW at the base case. Following the application of 3 DGs using CSA-SR, the real power loss is found to be 113.9630 MW which shows a reduction of 88.441MW which is equivalent to 43.77% as related to the base case. The results minimized the system losses while significantly enhancing the voltage profile of the bus voltages as a result of optimal siting and sizing of the DG. These show that the CSA-SR technique outperformed PSO, which was used to ascertain the effectiveness of the developed scheme.

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Section: Concept Of Voltage Stability Index (Vsi)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Distributed Generation Using Improved Cuckoo Search Algorithm

Timta,

Umar,

Timta

et al. 2024

PakJET

View full text Add to dashboard Cite

show abstract

“…In [5], the authors investigated the deployment of DGs in several rounds using a plant propagation algorithm while minimizing active power losses (APL) and reduced voltage drop. The researchers in [6] improved voltage stability index (VSI) along with the above two objectives by utilizing a newly introduced cloud model-based symbiotic organism search algorithm for DG deployment. However, ref.…”

Section: Introductionmentioning

confidence: 99%

Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System under a 24 h Load Horizon

et al. 2022

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Load variations in any power system result in loss escalation and voltage drops. With the sensible and optimal allocation of distributed generators (DGs), these problems could be considerably mitigated. It has been seen in existing methods that, ideally, the allocation of DGs has been carried out during fixed loads and constant power requirements. However, in real scenarios the loads are always variable and the allocation of DGs must be done in accordance with the variations of the connected load. Therefore, the current paper addresses the aforementioned problem by the distinctive optimal allocation of DGs for each variability of 24 h load horizon. However, a single exclusive solution is considered among all allocations of 24 h. The min-max regret concept has been utilized in order to deal with such a methodology. Altogether, 24 scenarios are analyzed wherein each scenario corresponds to a specific hour of the respective day. The optimal allocation of DGs in terms of their optimal sizing and placement has been carried out by using three algorithms including battle royale optimization (BRO), accelerated particle swarm optimization (APSO), and genetic algorithm (GA). The multi-objective optimization problem is evaluated on the basis of minimum value criterion of the multi-objective index (MO). MO comprises active and reactive power losses and voltage deviation. Hence, in order to find the robustness of the proposed technique, Conseil international des grands reseaux electriques’ (CIGRE) MV benchmark model incorporating 14 buses has been used considerably as a test network. In the end, the results of three proposed algorithms have been compared.

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“…In [5], the authors have investigated the deployment of DGs in several rounds using a plant propagation algorithm while minimized active power losses (APL) and reduced voltage drop. While the researchers in [6] improved voltage stability index (VSI) along with the above two objectives by utilizing a newly introduced cloud model-based symbiotic organism search algorithm for DGs deployment. However, [7] explored optimal DGs deployment after and before system reconfiguration.…”

Section: Introductionmentioning

confidence: 99%

Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System Under A 24h Load Horizon

Abbas¹,

Qaisar²,

Waqar³

et al. 2022

Preprint

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Load variations in any power system result in losses escalation and voltage drops. With the sensible and optimal allocation of distributed generators (DGs), these problems could be considerably mitigated. It has been seen in the priorly existing methods that ideally allocation of DGs has been carried out during fixed loads and constant power requirements. However, in real scenarios the loads are always variable and allocation of DGs must be done in accordance to the variations of the connected load. Therefore, the current paper addressed the aforementioned problem by distinctive optimal allocation of DGs for each variability of 24hour load horizon. However, the single exclusive solution is considered among all allocations of 24 hours. The min-max regret concept has been utilized in order to deal with such methodology. Altogether, 24 scenarios are analyzed wherein each scenario corresponds to a specific hour of the respective day. The optimal allocation of DGs in terms of their optimal sizing and placement has been carried out by using three algorithms including battle royal optimization (BRO), accelerated particle swarm optimization (APSO) and genetic algorithm (GA). The multi-objective optimization problem is evaluated on the basis of minimum value criterion of the multi-objective index (MO). MO comprises of active and reactive power losses and voltage deviation. Hence, in order to find robustness of proposed technique, Conseil international des grands réseaux electriques (CIGRE’s) MV benchmark model incorporating 14 buses has been considerably used as a test network. In the end the results of three proposed algorithms have been compared.

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A cloud model based symbiotic organism search algorithm for DG allocation in radial distribution network

Cited by 16 publications

References 44 publications

Optimal Allocation of Distributed Generation Using Improved Cuckoo Search Algorithm

Optimal Allocation of Distributed Generation Using Improved Cuckoo Search Algorithm

Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System under a 24 h Load Horizon

Min-Max Regret-Based Approach for Sizing and Placement of DGs in Distribution System Under A 24h Load Horizon

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