Power generation mix in Colombia including wind power: Markowitz portfolio efficient frontier analysis with machine learning
The Colombian power market is hydro-dominated since 67 % of the power produced comes from hydro-power sources. In addition, it has thermal power from natural gas and coal, and in the last years, alternative energy sources such as wind and solar have been introduced, although so far their share is no...
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Elsevier
2024-12-01
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| Series: | Journal of Open Innovation: Technology, Market and Complexity |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2199853124001963 |
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| author | Sergio Botero Botero Claudia María García Mazo Francisco Javier Moreno Arboleda |
| author_facet | Sergio Botero Botero Claudia María García Mazo Francisco Javier Moreno Arboleda |
| author_sort | Sergio Botero Botero |
| collection | DOAJ |
| description | The Colombian power market is hydro-dominated since 67 % of the power produced comes from hydro-power sources. In addition, it has thermal power from natural gas and coal, and in the last years, alternative energy sources such as wind and solar have been introduced, although so far their share is not significant. Due to this condition, the Colombian power market is very volatile and depends on weather conditions. Usually, in rainy seasons prices are low and power is available, while in dry seasons, prices are high and power can be scarce. One of the main advantages of the new energy sources is that they are complementary to hydro-power, in the case of wind regimes, they are higher during dry seasons and lower during rainy seasons. We propose a complementarity analysis in the energy mix using Markowitz Portfolio analysis to determine if the efficient frontier is improved by introducing wind power to the system and the traditional port-folio analysis is improved by introducing Machine Learning (ML) into calculations. Results show that wind power improves the return while minimizing risk. Therefore, wind power would significantly reduce prices in Colombia's power mix while reducing volatility. This work follows the Open Innovation (OI) paradigm, the intersection of Machine Learning, portfolio optimization, and renewable energy presents a promising landscape for research and practical applications. Continued interdisciplinary collaboration and innovation are essential for harnessing the full potential of these technologies for a sustainable energy future. |
| format | Article |
| id | doaj-art-ec4be7be93cb41529e661a8d2896607c |
| institution | Kabale University |
| issn | 2199-8531 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Open Innovation: Technology, Market and Complexity |
| spelling | doaj-art-ec4be7be93cb41529e661a8d2896607c2024-12-05T05:20:09ZengElsevierJournal of Open Innovation: Technology, Market and Complexity2199-85312024-12-01104100402Power generation mix in Colombia including wind power: Markowitz portfolio efficient frontier analysis with machine learningSergio Botero Botero0Claudia María García Mazo1Francisco Javier Moreno Arboleda2Departamento de Ingeniería de la Organización, Facultad de Minas, Universidad Nacional de Colombia, Sede Medellín, Av. 80 65-223, Medellín, Colombia; Corresponding author.Facultad de Administración, Politécnico Colombiano Jaime Isaza Cadavid, Carrera 48 No. 7–151, Medellín, ColombiaDepartamento de Ciencias de la Computación y de la Decisión, Facultad de Minas, Universidad Nacional de Colombia, Sede Medellín, Av. 80 65-223, Medellín, ColombiaThe Colombian power market is hydro-dominated since 67 % of the power produced comes from hydro-power sources. In addition, it has thermal power from natural gas and coal, and in the last years, alternative energy sources such as wind and solar have been introduced, although so far their share is not significant. Due to this condition, the Colombian power market is very volatile and depends on weather conditions. Usually, in rainy seasons prices are low and power is available, while in dry seasons, prices are high and power can be scarce. One of the main advantages of the new energy sources is that they are complementary to hydro-power, in the case of wind regimes, they are higher during dry seasons and lower during rainy seasons. We propose a complementarity analysis in the energy mix using Markowitz Portfolio analysis to determine if the efficient frontier is improved by introducing wind power to the system and the traditional port-folio analysis is improved by introducing Machine Learning (ML) into calculations. Results show that wind power improves the return while minimizing risk. Therefore, wind power would significantly reduce prices in Colombia's power mix while reducing volatility. This work follows the Open Innovation (OI) paradigm, the intersection of Machine Learning, portfolio optimization, and renewable energy presents a promising landscape for research and practical applications. Continued interdisciplinary collaboration and innovation are essential for harnessing the full potential of these technologies for a sustainable energy future.http://www.sciencedirect.com/science/article/pii/S2199853124001963VolatilityWind powerHydro-powerPortfolio theoryMachine learning |
| spellingShingle | Sergio Botero Botero Claudia María García Mazo Francisco Javier Moreno Arboleda Power generation mix in Colombia including wind power: Markowitz portfolio efficient frontier analysis with machine learning Journal of Open Innovation: Technology, Market and Complexity Volatility Wind power Hydro-power Portfolio theory Machine learning |
| title | Power generation mix in Colombia including wind power: Markowitz portfolio efficient frontier analysis with machine learning |
| title_full | Power generation mix in Colombia including wind power: Markowitz portfolio efficient frontier analysis with machine learning |
| title_fullStr | Power generation mix in Colombia including wind power: Markowitz portfolio efficient frontier analysis with machine learning |
| title_full_unstemmed | Power generation mix in Colombia including wind power: Markowitz portfolio efficient frontier analysis with machine learning |
| title_short | Power generation mix in Colombia including wind power: Markowitz portfolio efficient frontier analysis with machine learning |
| title_sort | power generation mix in colombia including wind power markowitz portfolio efficient frontier analysis with machine learning |
| topic | Volatility Wind power Hydro-power Portfolio theory Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2199853124001963 |
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