Neutrosophic logic in energy efficiency and the circular economy: uncertainty modeling in power grids

This study addresses the challenge of uncertainty, variability, and indeterminacy in smart grid energy management by applying neutrosophic logic as a novel optimization and detection framework. A computational methodology was developed to integrate renewable energy generation, battery storage, and consumption under neutrosophic modeling, comparing its performance with classical, fuzzy, and machine learning approaches. The neutrosophic economic dispatch model enabled improved generation scheduling resilience, optimized battery degradation prediction, and optimized charging cycles. In parallel, a neutrosophic detection mechanism was designed to identify energy losses, anomalous consumption, and potential fraud in near real-time. The case study results demonstrate that the neutrosophic approach significantly reduced operating costs (USD 8,500 versus USD 15,000–20,000 for other models), maximized the economic benefit of actual detections (USD 940, the highest among the tested models), and yielded the most favorable net balance (−USD 7,560). These results indicate that neutrosophic models outperform traditional and fuzzy approaches in both cost efficiency and system reliability.