Optimal location and sizing of PV sources in DC networks for minimizing greenhouse emissions in diesel generators

This paper addresses the problem of the optimal location and sizing of photovoltaic (PV) sources in direct current (DC) electrical networks considering time-varying load and renewable generation curves. To represent this problem, a mixed-integer nonlinear programming (MINLP) model is developed. The...

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Autores:
Montoya, Oscar Danilo
Grisales-Noreña, Luis Fernando
Gil-González, Walter
Alcalá, Gerardo
Hernandez-Escobedo, Quetzalcoatl
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/9378
Acceso en línea:
https://hdl.handle.net/20.500.12585/9378
https://www.mdpi.com/2073-8994/12/2/322
Palabra clave:
Artificial neural networks
Diesel generation
Direct current networks
Greenhouse emissions
Numerical optimization
Mixed-integer nonlinear programming photovoltaic plants
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc/4.0/
Description
Summary:This paper addresses the problem of the optimal location and sizing of photovoltaic (PV) sources in direct current (DC) electrical networks considering time-varying load and renewable generation curves. To represent this problem, a mixed-integer nonlinear programming (MINLP) model is developed. The main idea of including PV sources in the DC grid is minimizing the total greenhouse emissions produced by diesel generators in isolated areas. An artificial neural network is employed for short-term forecasting to deal with uncertainties in the PV power generation. The general algebraic modeling system (GAMS) package is employed to solve the MINLP model by using the CONOPT solver that works with mixed and integer variables. Numerical results demonstrate important reductions of harmful gas emissions to the atmosphere when PV sources are optimally integrated (size and location) to the DC grid.