Probabilistic Algorithm based on 2m+1 Point Estimate Method Edgeworth considering Voltage Confidence Intervals for Optimal PV Generation

• Autor(a) principal: Bautista, Luis Gustavo Cordero [UNESP]
• Data de Publicação: 2022
• Outros Autores: Soares, Joao, Baquero, John Fredy Franco [UNESP], Vale, Zita
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/PMAPS53380.2022.9810644; http://hdl.handle.net/11449/240554
• topic: 2m+1 Point Estimate Method Edgeworth; Optimal Probability PV Generation; Probabilistic Algorithm Optimization; Voltage Confidence Intervals

Photovoltaic (PV) systems widespread into distribution networks due to its environmentally friendly source of energy, cost-competitive option and system support benefits. However, traditional distribution networks were not designed to operate under a high penetration of intermittent generation posing technical challenges for grid operation and planning. Therefore, probabilistic tools become suitable to cater for uncertainties in generation and demand, thus, leading to a more realistic network representation. Furthermore, the need for harvesting potential energy in an uncertain environment are essential for an efficient grid operation. In this context, this work proposes a probabilistic algorithm based on 2m+1 Point Estimate Method Edgeworth to tackle technical issues considering voltage confidence levels that is used for maximizing PV generation. Tests in a IEEE 33 buses radial distribution system using the proposed probabilistic algorithm yields higher accuracy of cost probability distribution, voltage confidence intervals and a faster computational time when compared to Monte Carlo simulation.