Implementation of a hierarchical cluster model to analyze wind and solar availability in the department of Antioquia, Colombia

This research presents a systematic spatiotemporal assessment of solar radiation and wind speed patterns throughout the Department of Antioquia, Colombia, employing advanced machine learning methodologies to evaluate renewable resource potential. The investigation aligns with Colombia's strategic energy transition framework, which targets a 51% reduction in greenhouse gas emissions by 2030 relative to baseline projections. Through hierarchical clustering analysis, five distinct groups of municipalities were identified, revealing significant solar potential with radiation averages between 4.6 and 5.0 kWh/m 2 , reaching maximum values of 5.4 kWh/m 2 in the Magdalena Medio subregion. This solar availability exceeds 60% of Colombia's maximum reported values, indicating substantial potential for photovoltaic (PV) and solar thermal projects. In contrast, the wind resource assessment shows limited viability for utility-scale wind energy development, with speeds rarely exceeding 3 m/s even at a height of 100 m, resulting in capacity factors between 4.1% and 11.8% in coastal onshore and offshore areas, respectively. Temporal analysis across solstices and equinoxes confirms year-round consistency in solar radiation, attributed to the region's equatorial location, while maintaining stable patterns across the identified clusters. The demonstrated synergy between reliable solar resources and Antioquia's abundant hydroelectric infrastructure supports the development of hybrid systems, particularly solar-hydroelectric integration, with promising applications in both electricity generation and green hydrogen production. Furthermore, the Magdalena Medio and northern Urabá subregions emerge as prime candidates for solar energy projects, offering strategic opportunities for industrial-scale renewable deployment. This research provides crucial insights for Colombia's decarbonization roadmap, emphasizing Antioquia's significant solar energy potential as a cornerstone for transitioning towards a sustainable and diversified energy matrix. • A hierarchical cluster model is implemented to categorize groups based on wind speed and solar radiation. • Antioquia’s municipalities can be segmented into five clusters. • Antioquia's equatorial proximity simplifies annual radiation behavior, facilitating energy planning and project development with solar radiation patterns. • The study paves the way for future research on integrated solar energy systems and green hydrogen production in Antioquia.

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Publication Details

Journal
Case Studies in Chemical and Environmental Engineering
Published
2024-11-04
DOI
https://doi.org/10.1016/j.cscee.2024.101006
Citations
3
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.63
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article

Implementation of a hierarchical cluster model to analyze wind and solar availability in the department of Antioquia, Colombia

Alejandro Restrepo, Iván D. Bedoya, Andrés Amell, Adrián David Cogollo et al.
3 citations
Case Studies in Chemical and Environmental Engineering
Energy Load and Power Forecasting
0.63
article

Implementation of a hierarchical cluster model to analyze wind and solar availability in the department of Antioquia, Colombia

Alejandro Restrepo, Iván D. Bedoya, Andrés Amell, Adrián David Cogollo, D. Villegas
article en
3 citations

Abstract

This research presents a systematic spatiotemporal assessment of solar radiation and wind speed patterns throughout the Department of Antioquia, Colombia, employing advanced machine learning methodologies to evaluate renewable resource potential. The investigation aligns with Colombia's strategic energy transition framework, which targets a 51% reduction in greenhouse gas emissions by 2030 relative to baseline projections. Through hierarchical clustering analysis, five distinct groups of municipalities were identified, revealing significant solar potential with radiation averages between 4.6 and 5.0 kWh/m 2 , reaching maximum values of 5.4 kWh/m 2 in the Magdalena Medio subregion. This solar availability exceeds 60% of Colombia's maximum reported values, indicating substantial potential for photovoltaic (PV) and solar thermal projects. In contrast, the wind resource assessment shows limited viability for utility-scale wind energy development, with speeds rarely exceeding 3 m/s even at a height of 100 m, resulting in capacity factors between 4.1% and 11.8% in coastal onshore and offshore areas, respectively. Temporal analysis across solstices and equinoxes confirms year-round consistency in solar radiation, attributed to the region's equatorial location, while maintaining stable patterns across the identified clusters. The demonstrated synergy between reliable solar resources and Antioquia's abundant hydroelectric infrastructure supports the development of hybrid systems, particularly solar-hydroelectric integration, with promising applications in both electricity generation and green hydrogen production. Furthermore, the Magdalena Medio and northern Urabá subregions emerge as prime candidates for solar energy projects, offering strategic opportunities for industrial-scale renewable deployment. This research provides crucial insights for Colombia's decarbonization roadmap, emphasizing Antioquia's significant solar energy potential as a cornerstone for transitioning towards a sustainable and diversified energy matrix. • A hierarchical cluster model is implemented to categorize groups based on wind speed and solar radiation. • Antioquia’s municipalities can be segmented into five clusters. • Antioquia's equatorial proximity simplifies annual radiation behavior, facilitating energy planning and project development with solar radiation patterns. • The study paves the way for future research on integrated solar energy systems and green hydrogen production in Antioquia.

Case Studies in Chemical and Environmental EngineeringVol. 10
Universidad de Antioquia (CO)
Affordable and clean energy
Openalex Percentile: Top 31%
Energy Load and Power Forecasting
0.63
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