Impact of loading capability on optimal location of renewable energy systems distribution networks

Hemeida, Ashraf Mohamed; Bakry, Omima; Alkhalaf, Salem; Mikhaylov, Alexey; Zobaa, Ahmed F.; Senjyu, Tomonobu; Mikhailef, Saad; Dardeer, Mostafa

doi:10.1016/j.asej.2023.102340

Cited by 4 publications

(1 citation statement)

References 65 publications

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“…In 25 , a GWO and PSO approach for selecting the optimal location and sizing of three PV-DGs, that minimize losses and improve the voltage profile has been presented. In 26 , 27 , the influence of the optimal allocation of different DGs (with different active and reactive power capabilities) into the distribution network has been investigated, which has demonstrated that the DG injecting both real and reactive power has the best technical performance. The best location and size of the renewable resources have been determined by using multi-objective enhanced grey wolf optimizer ( MOEGWO) improved based on the logistic chaotic mapping integrated with fuzzy decision-making approach, which reduced the operation and emission costs by 23.34% and 34.78%, respectively, and increased the renewable hosting capacity by 7.62% 28 .…”

Section: Introductionmentioning

confidence: 99%

Multi-stage framework for optimal incorporating of inverter based distributed generator into distribution networks

Hamza,

Yousef,

Ali

et al. 2024

Sci Rep

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Recently, hydrogen-based distributed generators (DG) have gained significant attention for modern energy generation systems. These modem DGs are typically outfitted with power electronics converters, resulting in harmonic pollution. Furthermore, increasing the growth of modern nonlinear loads may result in exceeding the harmonic beyond the permitted level. This research proposes a framework for optimal incorporation of inverter-based distributed generation (a fuel cell connected to an AC distribution system) for minimizing power losses, enhancing the voltage profile, and limiting both total and individual harmonic distortion according to the IEEE-519 standard. In addition, for accounting system sustainability, the proposed framework considers load variation and the expected rise in demand. Therefore, the suggested framework comprises three stages, which include fundamental and harmonic power flow analysis. The first stage identifies the optimal size and location of the DG in relation to the base load operating condition. While, with the optimal DG of the first stage, the amount of harmonic pollution may violate the limits during a high level of nonlinear load penetration, as a result, the second stage resizes the DG, considering the connection bus of the first stage, to mitigate the harmonics and optimize the system at a higher level of nonlinear load penetration. Both the first and second stages are performed off-line, while the third stage optimizes the system operation during run time according to loading conditions, harmonic pollution, and the available DG capacity of the previous stages. DG’s harmonic spectrum is represented according to recently issued IEEE 1547-2018 for permissible DG’s current distortion limits. The suggested approach is applied and evaluated using an IEEE 33-bus distribution system for various combinations of linear and nonlinear loads. For run-time operation throughout the day, the presented framework reduces the energy losses from 5.281 to 2.452 MWh/day (about 53.57% energy savings). This saving is associated with voltage profile enhancement without violating the permissible standard levels of harmonics and other system constraints.

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Section: Introductionmentioning

confidence: 99%

Multi-stage framework for optimal incorporating of inverter based distributed generator into distribution networks

Hamza,

Yousef,

Ali

et al. 2024

Sci Rep

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Residential Parking Lot Pricing Strategy Considering Scheduling of Electric Vehicles

Mudgal,

Tiwari

2024

J. Inst. Eng. India Ser. B

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Optimizing energy-efficient grid performance: integrating electric vehicles, DSTATCOM, and renewable sources using the Hippopotamus Optimization Algorithm

Abdelaziz,

Ali,

Swief

et al. 2024

Sci Rep

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The rapid increase in renewable energy integration and electric vehicle (EV) adoption creates significant challenges for the stability and efficiency of power distribution networks. This study addresses the need for optimized placement and sizing of Electric Vehicle Charging Stations (EVCSs), photovoltaic (PV) systems, and Distribution Static Compensators (DSTATCOMs) to enhance grid performance. The motivation for this work arises from the fluctuating nature of renewable energy generation and the unpredictable demands of EV charging, which strain existing infrastructure. To address these challenges, we propose a novel optimization framework that introduces the Renewable Distributed Generation Hosting Factor (RDG-HF) and Electric Vehicle Hosting Factor (EV-HF) as key metrics. These metrics, combined with the Hippopotamus Optimization Algorithm (HO), enable strategic planning within the IEEE 69-bus system. Simulation results demonstrate that the integrated placement of EVCSs, PVs, and DSTATCOMs reduces power losses by up to 31.5% and reactive power losses by up to 29.2%. An economic analysis further reveals payback periods ranging from 2.7 to 10.4 years and potential profits of up to $1,052,365 over 25 years. These findings highlight the importance of optimized integration in improving both technical performance and long-term economic benefits for distribution networks.

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Impact of loading capability on optimal location of renewable energy systems distribution networks

Cited by 4 publications

References 65 publications

Multi-stage framework for optimal incorporating of inverter based distributed generator into distribution networks

Multi-stage framework for optimal incorporating of inverter based distributed generator into distribution networks

Residential Parking Lot Pricing Strategy Considering Scheduling of Electric Vehicles

Optimizing energy-efficient grid performance: integrating electric vehicles, DSTATCOM, and renewable sources using the Hippopotamus Optimization Algorithm

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