Optimum Position and Optimum Size of the Distributed Generators for Different Bus Network Using Genetic Algorithm

Gidd, Mukul M.; Mhetre, Santosh L.; Korachagaon, Iranna

doi:10.1109/iccubea.2018.8697595

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Talking about DG is a challenging task since, internationally, there is no general definition to categorize it within a single purpose, having to resort to the criteria established by several authors to reach an accurate and commonly used depiction within several texts [18,20]. Considering this premise, DG is defined as a renewable and small electric generator that can be connected to the transmission system and more frequently to the DS [21], being the appropriate generation to reduce environmental pollution and the most suitable for installation near the consumers [3,7]. The energy obtained through photovoltaic, wind, biomass, geothermal, and hydraulic generation (small-scale) is the most referenced when planning the development and installation of DG within a system; however, currently, it has been chosen to use technologies from non-renewable sources of low pollution as sources of DG, the same that are obtained by micro-turbines and combustion turbines, among others [9,18,20].…”

Section: Distributed Generation (Dg)mentioning

confidence: 99%

“…To obtain an ideal integration of DG, it is necessary to take into account its location, sizing, type, and the way of installing the DG [9]; in this case, when considering both the location and the sizing, it is possible to maximize the capacity of the DG without causing a decrease in the performance of the system, keeping the voltage profile within the admissible standard ranges [3,6]. Due to a lack of previous knowledge when installing DG, a random installation is sought, that is, without developing a previous study that shows its benefits, scope, and difficulties, incurring an inadequate structure of the DG power plants [21], which makes it necessary to define several restrictions so that the integration of the DG to the DS is developed in an adequate way [18]. In this sense, several strategies seek to optimally solve the problem of optimal placement and sizing of DG, improving the voltage profile [24,25].…”

Section: Integration Of Dg Into the Power Systemmentioning

confidence: 99%

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Localization and Sizing of Distributed Generation through a Genetic Algorithm to Improve Voltage Profile Using Ecuadorian Standards

Calvachi,

Tipán,

Jaramillo

2023

Energies

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The approach proposed for the development of this research work was based on the integration of Distributed Energy Generation (DG) into an energy distribution network, improving the voltage profile by establishing an optimal location and sizing of DG power plants, for which the use of a heuristic optimization method known as Genetic Algorithm (GA), which has several restrictions to limit its operation and thus achieve an optimal solution to the planned optimization problem, was resorted to. The development of a power flow allowed having the voltage measurement in the bars of the network before incorporating DG, allowing to verify that the voltage fluctuations present an improvement after the incorporation one by one of each DG central defined by the GA. The systems in which the voltage measurement was performed, as well as the verification of its fluctuations with and without DG once the GA was used, were the IEEE systems of 9 and 14 busbars, the latter the one used to demonstrate the scalability of the GA to locate and dimension DG without considering the number of busbars in a system.

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Section: Distributed Generation (Dg)mentioning

confidence: 99%

Section: Integration Of Dg Into the Power Systemmentioning

confidence: 99%

Localization and Sizing of Distributed Generation through a Genetic Algorithm to Improve Voltage Profile Using Ecuadorian Standards

Calvachi,

Tipán,

Jaramillo

2023

Energies

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“…At (Gidd et al, 2018) a methodology to optimize the optimal DG location and sizing in distribution networks is presented. The technique used is the Genetic Algorithm and for the power flow the Newton Raphson method has been used, both have been developed in MATLAB.…”

Section: Bibliographic Reviewmentioning

confidence: 99%

Optimization in the planning of electrical distribution networks with the incorporation of distributed generation

Barros

Pérez‐Rodríguez

2023

SIJIS

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This article presents the search, organization and analysis of the documentation, articles from magazines and conferences related to the optimization techniques applied in the planning of electrical distribution networks. The objective of this research is to review the state of the art of the significant aspects of the insertion of DG in the distribution system, the objectives and restrictions and how these affect the state of electrical networks. The information was acquired from available and recognized sources in the field of electrical engineering. The analysis of the publications was made chronologically from the beginnings to the current trends in the planning of electrical distribution networks; In addition, technical aspects were considered to classify the content, which allowed the information to be better extracted. The most relevant result was obtained that the most used techniques for solving the ODGP problem are the genetic algorithm and various heuristic algorithms.

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“…Distributed generation (DG) in a power system refers to the generation of electricity from many small, decentralized sources located close to the end-users of the electricity [1] or also known as dispersed generation and embedded generation [2]. These sources can include renewable energy technologies like solar panels, wind turbines, small-scale hydroelectric generators, and combined heat and power (CHP) systems, as well as conventional generators such as diesel engines and natural gas turbines [3]- [4]. Distributed generation can provide a range of benefits, including increased energy reliability, reduced transmission and distribution losses, and the potential for cleaner and more sustainable energy production.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Composite Compensation Under Load Variation in Power Transmission System.

Saiful Amri Ismail

2024

jes

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The power transmission system is in a stressed condition due to the increasing electricity demand. This stress, exacerbated by the deregulated power system environment, necessitates an urgent additional supply to maintain system adequacy. One important initiative to alleviate the transmission burden which meets the load demand can be rectified by composite compensation scheme. This paper showcases intelligent composite compensation under varying loads, emphasizing loss minimization. The approach integrates a loss control scheme involving Distributed Generation (DG) and Optimal Reactive Power Dispatch (ORPD) with multi-DG installation termed composite compensation, employing a novel optimization technique called Integrated Cloning Accelerated Mutation Evolutionary Programming (ICAMEP). The study identifies optimal locations and sizes for composite compensation in the power transmission system, demonstrating its superiority over traditional optimization techniques namely the evolutionary programming (EP) and artificial immune systems (AIS). Results are demonstrated for four cases involving single DG, 3 DGs and composite compensation validated on IEEE 30-Bus Reliability Test System (RTS). ICAMEP is superior over EP and AIS in achieving the highest loss reduction.

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Optimum Position and Optimum Size of the Distributed Generators for Different Bus Network Using Genetic Algorithm

Cited by 8 publications

References 4 publications

Localization and Sizing of Distributed Generation through a Genetic Algorithm to Improve Voltage Profile Using Ecuadorian Standards

Localization and Sizing of Distributed Generation through a Genetic Algorithm to Improve Voltage Profile Using Ecuadorian Standards

Optimization in the planning of electrical distribution networks with the incorporation of distributed generation

Intelligent Composite Compensation Under Load Variation in Power Transmission System.

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