Herramienta computacional para la determinación óptima de fuentes de generación renovable en redes de distribución.
It was identified that the optimization algorithm developed for generation integration modeling distributed in low voltage systems, developed by the research group GISEL of the Universidad del Norte, does not have an interface to establish points of connection of renewable energies in distribution n...
- Autores:
-
Coba Pinedo, Moisés Alejandro
Suárez Gutiérrez, Juan David
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad del Norte
- Repositorio:
- Repositorio Uninorte
- Idioma:
- spa
- OAI Identifier:
- oai:manglar.uninorte.edu.co:10584/8728
- Acceso en línea:
- http://hdl.handle.net/10584/8728
- Palabra clave:
- Generación distribuida
Optimización
Fuentes de energía no convencional
Distributed generation
Optimization
Unconventional energy sources
- Rights
- License
- Universidad del Norte
| Summary: | It was identified that the optimization algorithm developed for generation integration modeling distributed in low voltage systems, developed by the research group GISEL of the Universidad del Norte, does not have an interface to establish points of connection of renewable energies in distribution networks. To solve the problem mentioned, the design and implementation of a graphical interface is proposed to estimate the points of a distribution network in which renewable sources can be incorporated. This means an advantage for the design of distributed generation networks, as it will facilitate the inclusion of renewable sources at strategic points. For the design of the interface a development process is established, this is divided into four phases First phase, data collection of irradiance and wind speed, which are the variables of interest in photovoltaic and wind generation. Third phase, of selection and implementation of the optimization algorithm, for the selection of this algorithm three criteria are taken into account, which are: convergence capacity, the computational effort that requires execution and the ease of implementing the algorithm with the system of tests. Fourth phase, construction of the main interface and the auxiliary ones required where the results obtained from the optimization algorithm are shown. After the implementation and design of the algorithm, it was obtained that for the study system, the losses in the lines can be ideally reduced by up to 85% by implementing four generation points distributed in the system. |
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