Predictive expert assessments for large-scale battery storage system investments with conditional multi-facet fuzzy logarithmic least-squares and orthogonal metric robust aggregation

The lack of systematic analysis of the most important criteria affecting the performance of large-scale battery storage system investments creates uncertainty for decision-makers and reduces the effectiveness of investments. While there are limited studies addressing this issue in the literature, prioritizing performance-impacting criteria and evaluating alternative investment strategies stands out as a significant research gap. The primary objective of this study is to identify the most critical investment criteria affecting the performance of large-scale battery storage investments and to systematically evaluate the most suitable alternative investment strategies for these investments. To this end, expert opinions from senior executives at five international renewable energy companies are utilized, and the proposed decision-making model is constructed using this data. In the first stage of the model, the weights of the investment criteria are calculated using the logarithmic least squares method based on dynamic multi-facet fuzzy sets. Subsequently, alternative investment strategies are ranked using the robust aggregation technique based on orthogonal metrics, a rarely used technique in the literature, based on dynamic multi-facet fuzzy sets. This model better represents the multi-layered nature of uncertainty and integrates expert judgments more consistently compared to traditional fuzzy sets. The proposed hybrid fuzzy decision model identified energy density (0.318) and market volatility (0.274) as the two most influential criteria shaping investment priorities. Regarding strategic alternatives, maximizing renewable energy integration is identified as the most important strategy. These results reveal strategic orientations that have the potential to increase both technical efficiency and economic returns. This study, with its proposed model, not only strengthens the decision-support process but also fills a significant gap in the literature. It provides a guiding framework for policymakers and investors regarding which criteria to focus on and which strategies to prioritize in large-scale battery projects.