Transmission expansion planning of large power networks via constructive metaheuristics with security constraints and load uncertainty analysis

Assis, Fernando A.; Silva, Armando M. Leite da; Manso, Luiz A. F.; Castro, José F. C.

doi:10.1002/2050-7038.12595

Cited by 9 publications

(14 citation statements)

References 28 publications

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“…In order to analyze the performance of the proposed methodology considering different UML techniques (SOM, K‐means, and K‐medoids clustering), the composite reliability of the IEEE reliability test system (IEEE‐RTS) is initially evaluated 37 for two different generation and load scenarios. Then, a real large system, which corresponds to the southern region of Brazil, 38 is also considered in the evaluation of the proposed method. In all cases, the size N M = 150 is defined for the set Ω M , with a ratio of two success states to one failure state.…”

Section: Resultsmentioning

confidence: 99%

“…The system's reinforced configuration consists of 242 buses and 489 circuits, among transmission lines and transformers. Further details about this system can be found in Reference 38.…”

Section: Resultsmentioning

confidence: 99%

“…The data that support the findings of this study are openly available in Reference 37 and in “Sites.Google” at https://sites.google.com/view/southernbrazilian/data 38 …”

Section: Peer Reviewmentioning

confidence: 99%

“…Additionally, an equivalent subsystem from the southern region of Brazil is also used in order to demonstrate the benefits of applying the proposed method. This subsystem, which makes up the Brazilian interconnected system, corresponds to a real large subnetwork, proposed in Reference 38 to study transmission expansion planning problems.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised machine learning techniques applied to composite reliability assessment of power systems

Assis

Coelho

Rezende

et al. 2021

Int Trans Electr Energ Syst

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Summary Composite generation and transmission system reliability evaluation allows the assessment of the risks of system operation failure, taking into account the uncertainties associated with the availability of equipment. One of the great challenges faced in the use of techniques based on probabilistic assessment during the planning stages is related to the required computational costs. Depending on the reliability levels of the system under study and on the grid size, a large number of operation performance analyzes are necessary. In this sense, this article proposes a new and simple method to efficiently evaluate the composite reliability of electrical power networks. The nonsequential Monte Carlo simulation (MCS) method is combined with unsupervised machine learning (UML) techniques to reduce the computational effort involved in the process of estimating composite reliability indices. The proposed approach allows different unsupervised techniques to be employed, in order to obtain significant reductions in CPU times, without losing the accuracy of the estimated indices. The IEEE‐RTS system, considering the original load and generation and its modified version with the transmission network stressed, in addition to a real large system, is used for evaluating the performance of the proposed method. The results obtained with the use of three different classification techniques (Kohonen self‐organizing map, K‐means, and K‐medoids) are presented and analyzed.

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Section: Resultsmentioning

confidence: 99%

“…The system's reinforced configuration consists of 242 buses and 489 circuits, among transmission lines and transformers. Further details about this system can be found in Reference 38.…”

Section: Resultsmentioning

confidence: 99%

“…The data that support the findings of this study are openly available in Reference 37 and in “Sites.Google” at https://sites.google.com/view/southernbrazilian/data 38 …”

Section: Peer Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised machine learning techniques applied to composite reliability assessment of power systems

Assis

Coelho

Rezende

et al. 2021

Int Trans Electr Energ Syst

Self Cite

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“…Diversos trabalhos da literatura relacionada vêm propondo metodologias e estratégias para enfrentar esse problema. Dentre esses trabalhos, destaque pode ser dado ao emprego das metaheurísticas de otimização (Zhang et al, 2019;Assis et al, 2021), que apresentam uma boa relação de custobenefício em relação ao esforço de implementação envolvido e à qualidade das soluções identificadas para o pro-blema. Contudo, a depender das dimensões dos sistemas, nem mesmo as técnicas metaheurísticas garantem a identificação de boas soluções para o PET.…”

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Planejamento da Expansão de Sistemas Elétricos de Transmissão via Algoritmo Genético com Estratégias de Diversidade

Lopes

Assis

Schroeder

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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Transmission expansion planning (TEP) is an important study among the various electrical power system activities. However, solving the optimization problem resulting from TEP studies for real large electrical systems is a complex task, which involves the analysis of a large space of solutions. In this sense, the present work proposes the investigation of diversity strategies combined with the meta-heuristic Genetic Algorithm (GA) to solve the TEP problem. The Deterministic Crowding and K-means techniques are used to create different versions of GA with population diversity. A real and present-day electrical system, which corresponds to the geoelectric region of southern Brazil, is used to carry out performance studies and analyze the use of different diversity strategies. Resumo: O planejamento da expansão da transmissão (PET) configura um importante estudo dentre as diversas atividades realizadas em um sistema elétrico de potência. No entanto, solucionar o problema de otimização decorrente dos estudos PET para sistemas elétricos reais de grande porte é uma tarefa complexa, que envolve a análise de um grande espaço de soluções. Neste sentido, o presente trabalho propõe a investigação de estratégias de diversidade combinadas à metaheurística Algoritmo Genético (AG) para solução eficiente do problema PET. As técnicas Deterministic Crowding e K-means são utilizadas para criar diferentes versões do AG com diversidade populacional. Um sistema elétrico real e atual, que corresponde à região geoelétrica sul do Brasil, é utilizado para realizar os estudos de desempenho e analisar o emprego de diferentes estratégias de diversidade.

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Reliability evaluation of composite generation and transmission systems via binary logistic regression and parallel processing

Campos¹,

Assis²,

Silva³

et al. 2022

International Journal of Electrical Power & Energy Systems

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Transmission expansion planning of large power networks via constructive metaheuristics with security constraints and load uncertainty analysis

Cited by 9 publications

References 28 publications

Unsupervised machine learning techniques applied to composite reliability assessment of power systems

Unsupervised machine learning techniques applied to composite reliability assessment of power systems

Planejamento da Expansão de Sistemas Elétricos de Transmissão via Algoritmo Genético com Estratégias de Diversidade

Reliability evaluation of composite generation and transmission systems via binary logistic regression and parallel processing

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