Cleaner Engineering and Technology (Dec 2022)
Selection of energy storage technologies under neutrosophic decision environment
Abstract
The variability of renewable energy can be addressed by incorporating energy storage technologies (ESTs). Multiple energy storage technologies are available for renewable energy with wide disparity in attributes. Reports regarding the attributes are conflicting and complex, which makes it a multi-attribute decision analysis problem. To address this issue, new decision support tools are needed to aid in selecting ESTs based on their technical, environmental, and economic characteristics. In this study, a neutrosophic data envelopment analysis (NDEA) technique is developed and applied to selection of ESTs considering neutrosophic uncertainty inherent in new technologies. The decision tool systematically evaluates EST options by considering the expert's degree of satisfaction for higher technical performance, degree of dissatisfaction for lower technical performance and degree of indeterminacy for attaining better technical performance. Two case studies adapted from literature are used to illustrate the decision support tool. The first case study, which is used to demonstrate the tool for EST selection, involves the selection of thermal energy storage options. The second case study involves eight ESTs with different mechanisms: supercapacitor, flywheel, compressed air energy storage, pumped hydro energy storage, Li-ion battery, NiCd battery, NaS battery, and Pb-acid battery. The results reveal certain ESTs that are preferred over wide range of expert's perception to uncertainty in terms of dissatisfaction and indeterminacy. For example, Li-ion battery is DEA efficient if the risk tolerance for indeterminacy is greater than 40% and Pb-acid battery is DEA efficient at falsity tolerance greater than 80% and indeterminacy tolerance of less than 5%. These alternatives can be regarded as robust choices. Insights gained from the application of the decision tool can be used for guiding investment decisions in planning of renewable energy systems with energy storage.
