Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm

Sultana, Umbrin; Khairuddin, Azhar; Sultana, Beenish; Rasheed, Nadia; Qazi, Sajid Hussain; Malik, Nimra Riaz

doi:10.1016/j.energy.2018.09.083

Cited by 63 publications

(25 citation statements)

References 31 publications

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“…The outcome of the referred research work shows that the GOA is much better than its counterpart optimization algorithms in balancing the exploration versus exploitation properties. Moreover, recently the GOA algorithm was used to address some of the most significant engineering problems, such as optimal allocation of compensators [29], optimal placement of distributed generators [30], load forecasting [31], optimal sizing of DGs [32], and voltage-frequency control of an islanded MG [1]. These studies have verified the GOA effectiveness in solving the mentioned optimization problems much better than the old and traditional optimization methods.…”

Section: Grasshopper Optimization Algorithm For Active-reactive Powermentioning

confidence: 98%

Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm

et al. 2019

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Despite the vast benefits of integrating renewable energy sources (RES) with the utility grid, they pose stability and power quality problems when interconnected with the existing power system. This is due to the production of high voltages and current overshoots/undershoots during their injection or disconnection into/from the power system. In addition, the high harmonic distortion in the output voltage and current waveforms may also be observed due to the excessive inverter switching frequencies used for controlling distributed generator’s (DG) power output. Hence, the development of a robust and intelligent controller for the grid-connected microgrid (MG) is the need of the hour. As such, this paper aims to develop a robust and intelligent optimal power flow controller using a grasshopper optimization algorithm (GOA) to optimize the dynamic response and power quality of the grid-connected MG while sharing the desired amount of power with the grid. To validate the effectiveness of proposed GOA-based controller, its performance in achieving the desired power sharing ratio with optimal dynamic response and power quality is compared with that of its precedent particle swarm optimization (PSO)-based controller under MG injection and abrupt load change conditions. The proposed controller provides tremendous system’s dynamic response with minimum current harmonic distortion even at higher DG penetration levels.

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Section: Grasshopper Optimization Algorithm For Active-reactive Powermentioning

confidence: 98%

Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm

et al. 2019

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“…Furthermore, topics related to the optimal distribution and sizes of BSS have also been investigated in battery swapping studies. Sultana et al [93] used a novel Grasshopper optimizer algorithm to help a BSS reduce energy loss and increase voltage stability. However, obstacles to BSS development remain, such as standardization of EV battery packs, consumer recognition of the BSS model, and reliable estimation of battery health status.…”

Section: Battery Swappingmentioning

confidence: 99%

“…Conductive charging, inductive charging, and battery swapping are considered the three available charging options for BEV applications. Table 3 [63,74,79,81,89,90,93] summarizes the system parameters and shows the advantages and disadvantages of each three. Compared these three options, conductive charging is the most widely used charging approach.…”

Section: Comparison Of Charging Technologymentioning

confidence: 99%

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Technology Development of Electric Vehicles: A Review

et al. 2019

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To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution.

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“…They compared the performance of the GOA with other well-known optimization techniques like GA, DE, and PSO, and concluded that the GOA balances the exploitation and exploration process better than its counterparts. Furthermore, the GOA algorithm has also recently been applied towards addressing important engineering problems like optimal distribution system reconfiguration and distributed generation placement [11], the optimal allocation of compensators [12], load forecasting [13] and sizing of multiple distributed generation and battery swapping stations [40]. These studies have validated the effectiveness of the GOA in solving optimization problems better than the conventional optimization techniques.…”

Section: Grasshopper Optimization Algorithmmentioning

confidence: 99%

Optimal Voltage and Frequency Control of an Islanded Microgrid using Grasshopper Optimization Algorithm

et al. 2018

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Due to the lack of inertia and uncertainty in the selection of optimal Proportional Integral (PI) controller gains, the voltage and frequency variations are higher in the islanded mode of the operation of a Microgrid (MG) compared to the grid-connected mode. This study, as such, develops an optimal control strategy for the voltage and frequency regulation of Photovoltaic (PV) based MG systems operating in islanding mode using Grasshopper Optimization Algorithm (GOA). The intelligence of the GOA is utilized to optimize the PI controller parameters. This ensures an enhanced dynamic response and power quality of the studied MG system during Distributed Generators (DG) insertion and load change conditions. A droop control is also employed within the control architecture, alongside the voltage and current control loops, as a power-sharing controller. In order to validate the performance of the proposed control architecture, its effectiveness in regulating MG voltage, frequency, and power quality is compared with the precedent Artificial Intelligence (AI) based control architectures for the same control objectives. The effectiveness of the proposed GOA based parameter selection method is also validated by analyzing its performance with respect to the improved transient response and power quality of the studied MG system in comparison with that of the Particle Swarm Optimization (PSO) and Whales Optimization Algorithm (WOA) based parameter selection methods. The simulation results establish that the GOA provides a faster and better solution than PSO and WOA which resulted in a minimum voltage and frequency overshoot with minimum output current and Total Harmonic Distortion (THD).

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Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm

Cited by 63 publications

References 31 publications

Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm

Optimal Power Flow Controller for Grid-Connected Microgrids using Grasshopper Optimization Algorithm

Technology Development of Electric Vehicles: A Review

Optimal Voltage and Frequency Control of an Islanded Microgrid using Grasshopper Optimization Algorithm

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