Power Loss Minimization Using Optimal Placement and Sizing of Photovoltaic Distributed Generation Under Daily Load Consumption Profile with PSO and GA Algorithms

Khenissi, Imene; Sellami, Raida; Fakhfakh, Mohamed; Nèji, Rafik

doi:10.1007/s40313-021-00744-7

Cited by 16 publications

(14 citation statements)

References 29 publications

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“…The encircling behavior of dingoes, which involves the strategic positioning and coordination of the pack members, is modeled using mathematical Equations ( 6)- (10).…”

Section: Encirclingmentioning

confidence: 99%

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Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Ayanlade,

Ariyo,

Jimoh

et al. 2023

Sustainability

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Photovoltaic distributed generation (PVDG) is a noteworthy form of distributed energy generation that boasts a multitude of advantages. It not only produces absolutely no greenhouse gas emissions but also demands minimal maintenance. Consequently, PVDG has found widespread applications within distribution networks (DNs), particularly in the realm of improving network efficiency. In this research study, the dingo optimization algorithm (DOA) played a pivotal role in optimizing PVDGs with the primary aim of enhancing the performance of DNs. The crux of this optimization effort revolved around formulating an objective function that represented the cumulative active power losses that occurred across all branches of the network. The DOA was then effectively used to evaluate the most suitable capacities and positions for the PVDG units. To address the power flow challenges inherent to DNs, this study used the Newton–Raphson power flow method. To gauge the effectiveness of DOA in allocating PVDG units, it was rigorously compared to other metaheuristic optimization algorithms previously documented in the literature. The entire methodology was implemented using MATLAB and validated using the IEEE 33-bus DN. The performance of the network was scrutinized under normal, light, and heavy loading conditions. Subsequently, the approach was also applied to a practical Ajinde 62-bus DN. The research findings yielded crucial insights. For the IEEE 33-bus DN, it was determined that the optimal locations for PVDG units were buses 13, 25, and 33, with recommended capacities of 833, 532, and 866 kW, respectively. Similarly, in the context of the Ajinde 62-bus network, buses 17, 27, and 33 were identified as the prime locations for PVDGs, each with optimal sizes of 757, 150, and 1097 kW, respectively. Remarkably, the introduction of PVDGs led to substantial enhancements in network performance. For instance, in the IEEE 33-bus DN, the smallest voltage magnitude increased to 0.966 p.u. under normal loads, 0.9971 p.u. under light loads, and 0.96004 p.u. under heavy loads. These improvements translated into a significant reduction in active power losses—61.21% under normal conditions, 17.84% under light loads, and 33.31% under heavy loads. Similarly, in the case of the Ajinde 62-bus DN, the smallest voltage magnitude reached 0.9787 p.u., accompanied by an impressive 71.05% reduction in active power losses. In conclusion, the DOA exhibited remarkable efficacy in the strategic allocation of PVDGs, leading to substantial enhancements in DN performance across diverse loading conditions.

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“…The encircling behavior of dingoes, which involves the strategic positioning and coordination of the pack members, is modeled using mathematical Equations ( 6)- (10).…”

Section: Encirclingmentioning

confidence: 99%

“…Furthermore, Khenissi et al [10] studied the optimum capacity and allocation of PVDGs in a modified IEEE 14-bus DN. They used PSO and the GA as optimization approaches to diminish overall power loss and boost voltage and frequency profiles.…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Ayanlade,

Ariyo,

Jimoh

et al. 2023

Sustainability

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“…In [26], to minimize daily power losses on the modified IEEE 14-bus, solar DG allocation in Type-I was provided by using GA and PSO.…”

Section: B Literature Reviewmentioning

confidence: 99%

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

Pürlü

Türkay

2022

IEEE Access

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In this paper, two metaheuristic methods, genetic algorithm and particle swarm optimization, are proposed to determine the optimal locations, sizes and power factors of single and double distributed generation units. In line with the 2050 carbon neutral goal, the aim was to integrate renewable distributed energy sources such as photovoltaic panels and wind turbines into the distribution system with a high penetration level. In contrast to most studies based on constant loads and dispatchable generations, an application considering the seasonal uncertainties of generation and consumption was performed to minimize the annual energy losses and voltage deviations of the distribution network. In addition, dispatchable, controllable and fuel-based conventional resources were allocated to compare the contributions of renewable resources. These seasonal case studies with various constraints were applied to IEEE 33-bus radial distribution network. To verify the feasibility and robustness of the proposed algorithms, case studies for peak loads were created and compared with the literature studies. While all distributed generation sources were operated at both unity and optimum power factor in all case studies, zero power factor and leading power factor scenarios were examined for a peak load only. Photovoltaic applications without energy storage technologies have not been very efficient because of the uneven daily distribution of solar irradiance, especially insufficient irradiation in the evening and excessive irradiation at noon. However, wind energy applications are more reliable and feasible, because the wind speed distribution is relatively more uniform than that of solar irradiation, both seasonally and daily. In all cases, operating distributed generation sources at the optimal power factor provided better results than those operating at unity power factor. As a result, wind turbines operating at optimal power factors have been found to contribute better than photovoltaic systems and are almost as good as conventional sources with controllable power output. While both proposed algorithms yielded better results than those in the literature, particle swarm optimization was better than genetic algorithm in terms of providing the best solution, faster convergence and shorter running time.

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“…In general, the sum of the losses in all branches determines the total active loss, 𝑃 𝑙𝑜𝑠𝑠 𝑇 , dissipating in the DS, which is described in Eq. ( 16) [11]:…”

Section: Total Active Power Lossmentioning

confidence: 99%

“…A modified moth flame optimization was proposed for solving the DG integration to improve the DS performance in terms of system losses, voltage deviation, and emission [10]. To minimize overall power losses and improve voltage and frequency profiles, the authors concentrated on determining the best placement and size for a photovoltaic distributed generation using particle swarm optimization and the genetic algorithm [11]. The DS's voltage stability index and network reconfiguration were both improved simultaneously by utilizing a modified water cycle algorithm in order to reduce system power losses while taking into account all operational restrictions [12].…”

Section: Introductionmentioning

confidence: 99%

Optimal DG Integration in Power Distribution Systems Using Coronavirus Herd Immunity Optimizer

2022

IJIES

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In this study, the problem of finding an optimal location and size of a distributed generator (DG) in distribution systems with considering operational distribution system constraints is proposed with the objective of maximizing DG hosting capacity (MDHC), reducing system loss, and improving voltage stability index (VSI). The proposed objective function is formulated as a multi-objective mixed-integer nonlinear optimization in order to solve it simultaneously. To solve this problem, the coronavirus herd immunity optimizer (CHIO), a bio-inspired metaheuristic optimization method, is herein proposed to simultaneously tackle a discrete and continuous DG integration problem in distribution systems. Extensive simulations on an IEEE 69-node system with different load levels and DG numbers are performed using MATLAB software to evaluate the efficacy of the proposed method. The simulation results demonstrate that the proposed method efficiently improves overall distribution system performance when compared to different DG numbers and load levels. Furthermore, the CHIO optimization method shows encouraging results and almost obtains the best results in all proposed cases when compared with well-known metaheuristic optimization methods such as genetic algorithm (GA), the hunger games search (HGS), the chaotic neural network algorithm (CNNA), and the water cycle algorithm (WCA). The CHIO can successfully offer a notable solution for the DG integration problem, and the obtained results, for example in case 1, revealed outperforming the CHIO compared to other methods in terms of the MDHC (i.e., 99.999 %), voltage profile improvement (i.e., the minimum voltage magnitude of 0.9696 p.u), VSI improvement (i.e., 29.16 %), and system loss reduction (i.e., 66.95 %) compared with the base case, respectively.

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Power Loss Minimization Using Optimal Placement and Sizing of Photovoltaic Distributed Generation Under Daily Load Consumption Profile with PSO and GA Algorithms

Cited by 16 publications

References 29 publications

Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks

Optimal Allocation of Renewable Distributed Generations Using Heuristic Methods to Minimize Annual Energy Losses and Voltage Deviation Index

Optimal DG Integration in Power Distribution Systems Using Coronavirus Herd Immunity Optimizer

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