Optimal Placement and Sizing of Multiple Distributed Generators using Fuzzy Logic

Magadum, Rudresh B.; Kulkarni, D. B.

doi:10.1109/icees.2019.8719240

Cited by 19 publications

(10 citation statements)

References 9 publications

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“…System configuration is dependent on an operating voltage of 100 MVA, 12.66 kVA (L-L), and 60 Hz. Load and impedance data is provided at [9]. The total real and interactive power loads on the system are 3715 kW and 2300 kV.…”

Section: Test Systemmentioning

confidence: 99%

“…Essallah et al have presented a new method for optimal planning of DGs where researchers used the Voltage -Stability Margin-Index (VSMI) method to determine the optimum location and determine the size by MATLAB -curve-fitting -approximation [8]. Magadum and Kulkarni have proposed the fuzzy logic method to determine the optimal location and size of DGs [9]. Researches [6 and 10], have proposed the genetic algorithm for the optimal planning of DGs.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Placement and Size of Distributed Generators Based on Autoadd and PSO to Improve Voltage Profile and Minimize Power Losses

Nasser¹,

Ali²,

Alkhafaji³

2021

ETJ

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This work aims to improve the voltage profile and reduce electrical network losses through optimal planning of distributed generators. A new search algorithm (Autoadd) along with the (PSO) are introduced to choose the best location and size for distributed generators. Two systems are implemented; a 33-bus test network and a 30-bus of a local community in the city of Al- Diwaniyah. At the power flow, a solution is implemented using a fixed-point iteration method within an OpenDSS environment to check the performance of both networks. Moreover, the optimal location and size of the distributed generators are determined using Autoadd and PSO methods. The Autoadd method is implemented within the OpenDSS environment, while the (PSO) method is implemented within the MATLAB-OpenDSS environment through the com-interface. The validity and effectiveness of the proposed methods are validated by comparison with the published researches. The results have proven that the fixed-point method has achieved high efficiency and accuracy in terms of analyzing the power flow, whereas the (Autoadd) algorithm has achieved a better effect in terms of improving the voltage profile and minimizing losses.

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Section: Test Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Placement and Size of Distributed Generators Based on Autoadd and PSO to Improve Voltage Profile and Minimize Power Losses

Nasser¹,

Ali²,

Alkhafaji³

2021

ETJ

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“…Apart from technology selection, prudent DG planning in size and locations is also important in fulfilling the above potential applications. Although capability of generating active and reactive power vary across different DG techniques, a small amount sizing of DG can also enhance power supply efficiency on the order of 10% to 20% power loss reduction through distribution by placing in appropriate locations 48,69‐72 . However, inappropriate installation of DGs may result in under‐ or over‐voltage issues and enlarge mismatches between reactive power demand against system reactive power supply ability, while inadequate DG sizes could not fulfil their potentials and redundant allocation may result in huge investment costs.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Although capability of generating active and reactive power vary across different DG techniques, a small amount sizing of DG can also enhance power supply efficiency on the order of 10% to 20% power loss reduction through distribution by placing in appropriate locations. 48,[69][70][71][72] However, inappropriate installation of DGs may result in under-or over-voltage issues and enlarge mismatches between reactive power demand against system reactive power supply ability, while inadequate DG sizes could not fulfil their potentials and redundant allocation may result in huge investment costs. Clearly, decisions of DG technology types, locations, and sizes are highly coupled, yielding a considerate number of feasible solutions, which are distributed in a highdimensional and non-convex domain due to complexity caused by network topology, power flow, power system operation, and so on.…”

Section: Problem Formulationmentioning

confidence: 99%

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

Yang

Chen

et al. 2020

Int J Energy Res

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Summary Distributed generation (DG) has attracted significant attention due to its great potential for enhancing economical and technical performance of power systems and reducing dependence on fossil fuels. Optimal sizing and placement are critical for stimulating such potential, about which a considerable number of models and algorithms have been proposed in past literature. This paper attempts to undertake a comprehensive review on optimal sizing and placement of DG via a systematic methodology procedure, including definition and classifications of DG, modelling and problem formulation with different technical and economic criteria, and summary of optimization algorithms. Common features and distinctive characteristics of both models and methods are identified, followed by evaluations and comparisons based on their practical performance in various test systems. Selection of DG techniques with respect to application scenarios, indispensable and optional considerations in DG planning models, and pros and cons of algorithms are listed in tables for a clearer understanding. Lastly, a total of 107 algorithms are addressed, which are classified into five categories. Particular, hybrid methods can deal with complex engineering problems with multiple objective functions and constraints most effectively and robustly. Future research trends are also highlighted with the aim of providing a comprehensive and state‐of‐the‐art survey for researchers, engineers, and other stakeholders.

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“…Various research works have given different techniques to have better DG placement and sizing. Magadum and Kulkarni [6] address the issue of multiple DG placement and sizing with the goal of minimizing power loss while staying within a safe voltage range. The solution for the DG placement uses fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of solar and wind distributed generation using particle swarm optimization technique

Rekha¹,

Byalihal²

2023

IJECE

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<span lang="EN-US">Power demand in the current days is increasing more and more where the conventional power generation systems are failing to meet these power demands due to less availability of non-renewable resources. Hence, many of the researchers are working on the distributed generation (DG) by using renewable resources like wind and solar. The penetration towards wind, solar DG faced challenging situations during power generation due to uncertainty in the wind speed and solar radiation. Recent studies have predicted that the combination of both solar and wind can lead to better performance. However, the sizing and placement of DG systems is necessary to achieve efficiency otherwise the systems may lead to adverse effects in distribution networks. This paper introduced the solar DG, wind DG and hybrid (solar and wind) DG system. The particle swarm optimization technique is used to size and place the DG because of its parallel search capability. Also, the combination of wind-solar DG gives better DG sizing in the respective DG location. The voltage profile of these DG systems has shown better results for the efficient power system. In comparison to conventional DG systems, the suggested hybrid DG system is capable of minimizing power loss and maintaining voltage profile.</span>

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Optimal Placement and Sizing of Multiple Distributed Generators using Fuzzy Logic

Cited by 19 publications

References 9 publications

Optimal Placement and Size of Distributed Generators Based on Autoadd and PSO to Improve Voltage Profile and Minimize Power Losses

Optimal Placement and Size of Distributed Generators Based on Autoadd and PSO to Improve Voltage Profile and Minimize Power Losses

Modelling, applications, and evaluations of optimal sizing and placement of distributed generations: A critical state‐of‐the‐art survey

Optimal allocation of solar and wind distributed generation using particle swarm optimization technique

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