Loading Margin Sensitivity in Relation to the Wind Farm Generation Power Factor for Voltage Preventive Control

Silva, Victor R. Neumann; Kuiava, Rôman

doi:10.1007/s40313-019-00507-5

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…Considering the framework of different types of DGs in this study, the DG unit's power factor (PF DG ) is determined according to operating conditions as following: First Type: PF DG = 1 Second Type: PF DG = 0 Third, fourth Type: 0.3 < PF DG < 0.95 [38].…”

Section: Power Factor Constraintsmentioning

confidence: 99%

Improving the Techno-Economic Pattern for Distributed Generation-Based Distribution Networks via Nature-Inspired Optimization Algorithms

Hassan

Othman

Bendary

et al. 2022

Technol Econ Smart Grids Sustain Energy

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The massive increase in the utilization of Distributed Generation (DG) units in the traditional Electric Distribution Networks (EDNs) enforces the distribution companies' operators to enhance the technical performance of EDNs while considering economic perspectives. This challenge paves the way for developing a multi-objective optimization platform to tackle the techno-economic problems while respecting system uncertainties as well as the operational policy of the distribution companies. As a motivating solution for this multi-objective problem, this paper introduces the application of three natureinspired algorithms as multi-objective optimization techniques for enhancing the techno-economic performance of EDNs through the integration of multiple Renewable Energy Resources (RERs). Grasshopper Optimization Algorithm (GOA), Salp Swarm Algorithm (SSA) and Moth Flame Optimization Algorithm (MFO), have been employed in this comparative study to minimize the active power losses, enhance the Fast Voltage Stability Index (FVSI) and reduce the total costs, considering the penetration level specified margin as well as and the framework of the DG units' operating power factor constraints. The proposed algorithms have been implemented in the MATLAB environment and applied on various benchmark IEEE test systems (33-bus, 57-bus and 300-bus) as a mimic, small and large EDNs. A realistic part of the Egyptian distribution network (171-bus) is also introduced as a practical, applicable case study. The attained results show that the suggested optimization platform especially using MFO, is more effective and successful in determining and finding the optimal locations and capacities of different DG types for getting the optimal value of the objective function in minimum time within a minimum number of iterations.

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Section: Power Factor Constraintsmentioning

confidence: 99%

Improving the Techno-Economic Pattern for Distributed Generation-Based Distribution Networks via Nature-Inspired Optimization Algorithms

Hassan

Othman

Bendary

et al. 2022

Technol Econ Smart Grids Sustain Energy

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“…The two WTs can generate both active and reactive power. The active power generation capacity is the same as Case 2; however, the range of power factor for WTs are extended from 0.95 lag to 0.95 lead as conducted in [38,39].…”

mentioning

confidence: 99%

Optimal Design and Operation of Wind Turbines in Radial Distribution Power Grids for Power Loss Minimization

Phan,

Duong,

Doan

et al. 2024

Applied Sciences

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This research proposes a strategy to minimize the active power loss in the standard IEEE 85-node radial distribution power grid by optimizing the placement of wind turbines in the grid. The osprey optimization algorithm (OOA) and walrus optimization algorithm (WOA) are implemented to solve the problem. The two algorithms are validated in three study cases of placing two wind turbines (WTs) in the system for power loss reduction. Mainly, in Case 1, WTs can only produce active power, while in Case 2 and Case 3, WTs can supply both active and reactive power to the grid with different ranges of power factors. In Case 4, the best-applied methods between the two are reapplied to reach the minimum value of the total energy loss within one year. Notably, this case focuses on minimizing the total power loss for each hour in a day under load demand variations and dynamic power supply from WTs. On top of that, this case uses two different sets of actual wind power data acquired from the Global Wind Atlas for the two positions inherited from the previous case. Moreover, the utilization of wind power is also evaluated in the two scenarios: (1) wind power from WTs is fully used for all values of load demand, (2) and wind power from WTs is optimized for each load demand value. The results in the first three cases indicate that the WOA achieves better minimum, mean, and maximum power losses for the two cases than the OOA over fifty trial runs. Moreover, the WOA obtains an excellent loss reduction compared to the Base case without WTs. The loss of the base system is 224.3 kW, but that of Case 1, Case 2, and Case 3 is 115.6, 30.6 kW, and 0.097 kW. The placement of wind turbines in Case 1, Case 2, and Case 3 reached a loss reduction of 48.5%, 84.3%, and 99.96% compared to the Base case. The optimal placement of WTs in the selected distribution power grid has shown huge advantages in reducing active power loss, especially in Case 3. For the last study case, the energy loss in a year is calculated by WSO after reaching hourly power loss, the energy loss in a month, and the season. The results in this case also indicate that the optimization of wind power, as mentioned in Scenario 2, results in a better total energy loss value in a year than in Scenario 1. The total energy loss in Scenario 2 is reduced by approximately 95.98% compared to Scenario 1. So, WOA is an effective algorithm for optimizing the placement and determining the power output of wind turbines in distribution power grids to minimize the total energy loss in years.

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Voltage Preventive Control Strategy Based on the Optimization of Wind Farms Power Factor

Silva

Kuiava

Ramos

et al. 2023

J Control Autom Electr Syst

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Loading Margin Sensitivity in Relation to the Wind Farm Generation Power Factor for Voltage Preventive Control

Cited by 7 publications

References 24 publications

Improving the Techno-Economic Pattern for Distributed Generation-Based Distribution Networks via Nature-Inspired Optimization Algorithms

Improving the Techno-Economic Pattern for Distributed Generation-Based Distribution Networks via Nature-Inspired Optimization Algorithms

Optimal Design and Operation of Wind Turbines in Radial Distribution Power Grids for Power Loss Minimization

Voltage Preventive Control Strategy Based on the Optimization of Wind Farms Power Factor

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