Probabilistic load elasticity analysis in low voltage distribution networks with high penetration of photovoltaic micro generation

Camilo, Fernando M.; Castro, Rui; Almeida, M.E.; Pires, V. Fernão

doi:10.1016/j.ijepes.2019.06.002

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Under this background, the penetration level of new equipment, i.e., rooftop photovoltaic, electric vehicle, and energy storage in LVDN increases gradually [3][4][5]. The development of these devices can effectively alleviate the pressure of environmental pollution and energy tension, but it has brought impacts and challenges to the safe operation and power supply quality of LVDN [6][7]. In order to fully dig the potential of distributed energy resources meanwhile operating the grid in an efficient and reliable manner, a high-level operation and maintenance management in LVDN is needed [8].…”

Section: Introductionmentioning

confidence: 99%

User-Transformer Connectivity Relationship Identification Based on Knowledge-Driven Approaches

et al. 2022

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Accurate user-transformer connectivity relationship (UTCR) plays a key role in fine management of low-voltage distribution network (LVDN) i.e., load expansion, line loss management, and electrical service restoration after outage. Limited data and low discriminability and noise in data increase the difficulty to identify UTCR for the existing data analytics methods. To overcome these hurdles, this paper proposes a novel UTCR algorithm which combining the data preprocessing with multi-dimensional priori knowledge based on voltage characteristics in LVDN. Firstly, the prior knowledge related to UTCR are refined on account of voltage correlation characteristics of users at different locations to provide theoretical foundation. Then, Z-score and principal component analysis are combined to standardize and extract features from the original voltage data to magnify the differences between data and reduce the impact of data noise. Further, on the basis of the prior knowledge of voltage correlation characteristics, a knowledge-driven identification model is proposed to identify users with wrong UTCR and their real UTCR. Finally, the performance of the proposed algorithm is verified on simulated LVNDs. The comparison analysis between the proposed method and other published methods and the impact of the number of principal components on the identification accuracy are also investigated. The results indicate that the proposed method achieves higher recognition accuracy than other published methods with low discriminability and noise in data.INDEX TERMS User-transformer connectivity relationship identification, low-voltage distribution network, data pre-processing, voltage correlation characteristics, knowledge-driven approaches

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Section: Introductionmentioning

confidence: 99%

User-Transformer Connectivity Relationship Identification Based on Knowledge-Driven Approaches

et al. 2022

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“…O programa desenvolvido permite previsões probabilísticas de fluxos de energia nas várias seções dos alimentadores de distribuição e perfis de tensão em todos os nós de uma rede. Camilo et al (2019) realiza uma pesquisa conjunta sobre o efeito da geração FV, utilizando dados reais no inverno e no verão, e das cargas residenciais do consumidor em sobretensões e desequilíbrios de tensão na rede de distribuicão de baixa tensão, utilizando SMC que produziu 10000 amostras. Neste trabalho, foi realizada uma comparação entre a carga modelada pela distribuição normal e o modelo ZIP, concluindo que o efeito da modelagem de carga nãoé muito significativo quando se trata de prever sobretensões ou subtensões em uma rede de distribuição de baixa tensão desequilibrada com um alto nível de microgeração fotovoltaica.…”

Section: Introductionunclassified

Impacto do Agrupamento de Dados da Geração Solar Fotovoltaica no Problema do Fluxo de Potência Trifásico

Costa

Oliveira

Ferreira

et al. 2020

Anais Do Simpósio Brasileiro De Sistemas Elétricos 2020

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Este artigo tem como objetivo investigar o impacto do agrupamento de dados de geração Fotovoltaica (FV) na solução do Fluxo de Potência Trifásico (FPT). Para tanto, os dados históricos da geração fotovoltaica são agrupados através da técnica de agrupamento k-means. Os resultados obtidos são comparados com a Simulação de Monte Carlo (SMC) onde um sistema de 37 barras modificado é utilizado para testar a eficácia da metodologia proposta.

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Consumer phase identification under incomplete data condition with dimensional calibration

Zhou

Zhang

et al. 2021

International Journal of Electrical Power & Energy Systems

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Probabilistic load elasticity analysis in low voltage distribution networks with high penetration of photovoltaic micro generation

Cited by 4 publications

References 26 publications

User-Transformer Connectivity Relationship Identification Based on Knowledge-Driven Approaches

User-Transformer Connectivity Relationship Identification Based on Knowledge-Driven Approaches

Impacto do Agrupamento de Dados da Geração Solar Fotovoltaica no Problema do Fluxo de Potência Trifásico

Consumer phase identification under incomplete data condition with dimensional calibration

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