Forecasts Plus Assessments of Renewable Generation Performance, the Effect of Earth's Geographic Location on Solar and Wind Generation
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| dc.contributor.author | Berna, C.
|
es_ES |
| dc.contributor.author | Álvarez-Piñeiro, Lucas
|
es_ES |
| dc.contributor.author | Blanco-Muelas, David
|
es_ES |
| dc.date.accessioned | 2025-02-26T19:09:54Z | |
| dc.date.available | 2025-02-26T19:09:54Z | |
| dc.date.issued | 2025-02 | es_ES |
| dc.identifier.uri | http://hdl.handle.net/10251/214875 | |
| dc.description.abstract | [EN] Featured Application Applicating stochastic modeling to address the interannual variability and reliability challenges of integrating solar and wind resources into renewable energy systems. The identification of low-production periods emphasizes the importance of storage and generation efficiency, supporting sustainable planning and helping identify ideal deployment locations while adapting to geographical and climatic variations.Abstract Solar and wind resources are critical for the global transition to net-zero emission energy systems. However, their variability and unpredictability pose challenges for system reliability, often requiring fossil fuel-based backups or energy storage solutions. The mismatch between renewable energy generation and electricity demand necessitates analytical methods to ensure a reliable transition. Sole reliance on single-year data is insufficient, as it does not account for interannual variability or extreme conditions. This paper explores probabilistic modeling as a solution to more accurately assess renewable energy availability. A 22-year dataset is used to generate synthetic data for solar irradiance, wind speed, and temperature, modeled using statistical probability distributions. Monte Carlo simulations, run 93 times, achieve 95% confidence and confidence levels, providing reliable assessments of renewable energy potential. The analysis finds that during Dunkelflaute periods, in high-solar and high-wind areas, DF events average 20 h in the worst case, while low-resource regions may experience DF periods lasting up to 48 h. Optimal energy mixes for these regions should include 15-20% storage and interconnections to neighboring areas. Therefore, stochastic consideration and geographic differentiation are essential analyses to address these differences and ensure a reliable and resilient renewable energy system. | es_ES |
| dc.language | Inglés | es_ES |
| dc.publisher | MDPI AG | es_ES |
| dc.relation.ispartof | Applied Sciences | es_ES |
| dc.rights | Reconocimiento (by) | es_ES |
| dc.subject | Monte Carlo techniques | es_ES |
| dc.subject | Uncertainty analysis | es_ES |
| dc.subject | Climatic regions | es_ES |
| dc.subject | Earth mapping | es_ES |
| dc.subject | Electric generation | es_ES |
| dc.subject | Renewable energy | es_ES |
| dc.subject | Wind power | es_ES |
| dc.subject | Solar photovoltaic | es_ES |
| dc.subject | Dunkelflaute | es_ES |
| dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
| dc.title | Forecasts Plus Assessments of Renewable Generation Performance, the Effect of Earth's Geographic Location on Solar and Wind Generation | es_ES |
| dc.type | Artículo | es_ES |
| dc.identifier.doi | 10.3390/app15031450 | es_ES |
| dc.rights.accessRights | Abierto | es_ES |
| dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
| dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials | es_ES |
| dc.description.bibliographicCitation | Berna, C.; Álvarez-Piñeiro, L.; Blanco-Muelas, D. (2025). Forecasts Plus Assessments of Renewable Generation Performance, the Effect of Earth's Geographic Location on Solar and Wind Generation. Applied Sciences. 15(3). https://doi.org/10.3390/app15031450 | es_ES |
| dc.description.accrualMethod | S | es_ES |
| dc.relation.publisherversion | https://doi.org/10.3390/app15031450 | es_ES |
| dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
| dc.description.volume | 15 | es_ES |
| dc.description.issue | 3 | es_ES |
| dc.identifier.eissn | 2076-3417 | es_ES |
| dc.relation.pasarela | S\541403 | es_ES |
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