Alternative Generation Sources Portfolio: Optimal Resources Allocation and Risk Analysis Supported by Genetics Algorithms
The natural resources characteristics and current economic factors encourage investments in alternative sources of electric power generation in Brazil. Different technologies can compose a portfolio of generating plants with energetic synergism as a function of the seasonal diversity of their potent...
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| Veröffentlicht in: | Revista IEEE América Latina 2016-07, Vol.14 (7), p.3232-3241 |
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| container_title | Revista IEEE América Latina |
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| creator | Steinle Camargo, Luiz Armando Soares Ramos, Dorel Guarnier, Ewerton Ishida, Sergio Matsudo, Eduardo |
| description | The natural resources characteristics and current economic factors encourage investments in alternative sources of electric power generation in Brazil. Different technologies can compose a portfolio of generating plants with energetic synergism as a function of the seasonal diversity of their potential production. In such portfolios, it is sought to obtain financial gains by virtue of complementarity generation among candidates sources, under investor's pre-established risk control criteria. From this perspective, our study aims to present an optimization model - supported by genetic algorithms - to define the optimal financial resources allocation for composing renewable sources portfolio (wind, small hydro and biomass cogeneration), given a specified budget and risk-aversion criteria measured by means of the Conditional Value-at-Risk. Case studies involving the cited sources illustrate the application of the model and its potential for supporting analysis and decision making. |
| doi_str_mv | 10.1109/TLA.2016.7587625 |
| format | Article |
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| subjects | Alternatives Sources Biological system modeling Computational modeling Genetic algorithms Investment Investment Analysis Market Risks Portfolio Composition Portfolios Resource management Risk analysis |
| title | Alternative Generation Sources Portfolio: Optimal Resources Allocation and Risk Analysis Supported by Genetics Algorithms |
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