A GIS-MCDA Approach Addressing Economic-Social-Environmental Concerns for Selecting the Most Suitable Compressed Air Energy Storage Reservoirs

• Main Author: Matos, Catarina R.
• Publication Date: 2021
• Other Authors: Carneiro, Júlio F., Silva, Patrícia Pereira da, Henriques, Carla O.
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://hdl.handle.net/10316/101520; https://doi.org/10.3390/en14206793
• topic: compressed air energy storage; potential underground reservoirs; economic-socialenvironmental concerns; multi-criteria decision analysis; simple additive weighting; sensibility analysis

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.