Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming

• Autor(a) principal: Vega, Fausto Alfredo Canales
• Data de Publicação: 2020
• Outros Autores: Jurasz, Jakub, Kies, Alexander, Beluco, Alexandre, Arrieta-Castro, Marco, Peralta-Cayón, Andrés
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/211667
• topic: Energia elétrica : Geração; Sistema híbrido de energia; Complementaridade energética; Energia renovável; Análise de correlação; Programação por compromisso; Energetic complementarity; Renewable energy; Variable renawables; Geographic information systems

Renewable energy sources have shown remarkable growth in recent times in terms of their contribution to sustainable societies. However, integrating them into the national power grids is usually hindered because of their weather-dependent nature and variability. The combination of different sources to profit from their beneficial complementarity has often been proposed as a partial solution to overcome these issues. Thus, efficient planning for optimizing the exploitation of these energy resources requires different types of decision support tools. A mathematical index for assessing energetic complementarity between multiple energy sources constitutes an important tool for this purpose, allowing a comparison of complementarity between existing facilities at different planning stages and also allowing a dynamic assessment of complementarity between variable energy sources throughout the operation, assisting in the dispatch of power supplies. This article presents a method for quantifying and spatially representing the total temporal energetic complementarity between three different variable renewable sources, through 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 a case study during the year of 2015.