Probabilistic load flow for photovoltaic distributed generation using the Cornish-Fisher expansion
This paper shows that in order to solve a probabilistic load flow in radial distribution networks, it is necessary to apply effective techniques that take into account their technical constraints. Among these constraints, voltage regulation is one of the principal problems to be addressed in photovo...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universidad de Huelva (UHU) |
| Repositorio: | Arias Montano. Repositorio Institucional de la Universidad de Huelva |
| Idioma: | inglés |
| OAI Identifier: | oai:ariasmontano.uhu.es:10272/24061 |
| Acceso en línea: | https://hdl.handle.net/10272/24061 |
| Access Level: | acceso abierto |
| Palabra clave: | Shuffled frog-leaping algorithm Monte Carlo method Biomass Gas engine Probabilistic load flow Three-phase load flow |
| Sumario: | This paper shows that in order to solve a probabilistic load flow in radial distribution networks, it is necessary to apply effective techniques that take into account their technical constraints. Among these constraints, voltage regulation is one of the principal problems to be addressed in photovoltaic distributed generation. Probabilistic load flows can be solved by analytical techniques as well as the Monte Carlo method. Our research study applied an analytical method that combined the cumulant method with the Cornish-Fisher expansion to solve this problem. The Monte Carlo method is used to compare the results of analytical method proposed. To evaluate the performance of photovoltaic distributed generation, this paper describes a probabilistic model that takes into account the random nature of solar irradiance. Therefore, load and photovoltaic distributed generation are modelled as independent/dependent random variables. The results obtained show that the technique proposed gave a better performance than the Monte Carlo method. This technique provided satisfactory solutions with a smaller number of iterations. Therefore, convergence was rapidly attained and computational cost was lower than that required for the Monte Carlo method. Besides, the results revealed how the Cornish-Fisher expansion had a better performance than the Gram-Charlier expansion, when input random variables were non-Gaussian. |
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