Abstract
This paper presents a method for spatially representing the total temporal energetic complementarity between three different variable renewable sources, by means of an index created from correlation coefficients and compromise programming. The method is employed to study the complementarity of wind speed, solar radiation and surface runoff on a monthly scale using continental Colombia as case study, during the year of 2015. Results show that the combination of solar radiation and surface runoff presented the highest energetic complementarity during this year, heavily influenced by El Niño phenomenon.
Keywords energetic complementarity, renewable energy, variable renewables, correlation, compromise programming, geographic information systems
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Energy Proceedings