Catarina R. Matos

Dr. Catarina R. Matos

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Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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23 November 2018 in Journal of Iberian Geology
Verified
Journal Article
Closed access
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MMMafalda Miranda
Mafalda Miranda
CMCatarina R. Matos
Catarina R. Matos
NRNelson Rodrigues
Nelson Rodrigues
APA. J. S. C. Pereira
A. J. S. C. Pereira
JCJosé J. Costa
José J. Costa

18 October 2021 in Energies
Verified
Journal Article
Open access
PDF file
CMCatarina R. Matos
Catarina R. Matos
JCJúlio Carneiro
Júlio Carneiro
PSPatricia Pereira da Silva
Patricia Pereira da Silva
CHCarla Oliveira Henriques
Carla Oliveira Henriques
This article presents an assessment of the most suitable compressed air energy storage (CAES) reservoirs and facilities to better integrate renewable energy into the electricity grid. The novelty of this study resides in selecting the best CAES reservoir sites through the application of a multi-criteria decision aid (MCDA) tool, specifically the simple additive weighting (SAW) method. Besides using geographic information systems (GIS) spatial representation of potential reservoir areas, for the MCDA method, several spatial criteria, environmental and social constraints, and positive incentives (e.g., the proximity to existing power generation facilities of renewable energy sources) were contemplated. As a result, sixty-two alternatives or potential reservoir sites were identified, and thirteen criteria (seven constraints and six incentives) were considered. The final stage of this study consisted of conducting a sensitivity analysis to determine the robustness of the solutions obtained and giving insights regarding each criterion’s influence on the reservoir sites selected. The three best suitable reservoir sites obtained were the Monte Real salt dome, Sines Massif, and the Campina de Cima—Loulé salt mine. The results show that this GIS-MCDA methodological framework, integrating spatial and non-spatial information, proved to provide a multidimensional view of the potential reservoir CAES systems incorporating both constraints and incentives.