Contributions to variable renewable energy systems simulation, design, and integration under uncertainty and stochasticity
This dissertation presents the development of a novel simulation methodology for solar irradiance and air temperature time series as well as the integration of optimal control and machine learning methodologies to support the decision-making process of DERs (i.e. operative decisions) as well as the...
- Autores:
-
Ramírez Arias, Andrés Felipe
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2021
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/55844
- Acceso en línea:
- http://hdl.handle.net/1992/55844
- Palabra clave:
- Simulación
Irradiancia solar
Temperatura del aire
Control
Aprendizaje automático
Toma de decisiones
Sistemas de energía renovable
Ingeniería
- Rights
- openAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
| Summary: | This dissertation presents the development of a novel simulation methodology for solar irradiance and air temperature time series as well as the integration of optimal control and machine learning methodologies to support the decision-making process of DERs (i.e. operative decisions) as well as the decisions taken in the transmission network (i.e. investment and planing decisions). For instance DERs, such as virtual power plants (VPPs), participate in the aggregated market to provide services (i.e. Complementary services, flexibility services to guarantee the energy supply, among others.) to the electricity system, particularly when there is a lack of diversification or the system is heavily invested in non-renewable energy sources. |
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