Risk Assessment of Grid-Integrated Energy Service Projects: A Hybrid Indicator-Based Fuzzy-Entropy-BP Evaluation Framework

Grid-integrated energy service (GIES) projects are characterized by strong cross-energy coupling and long investment horizons, resulting in multidimensional and nonlinear risk profiles. To address these challenges, this study develops an indicator-based risk evaluation framework by integrating an entropy–back-propagation (BP) combined weighting method with fuzzy matter-element theory. A 30-indicator system covering economic, environmental, and safety and reliability dimensions is constructed to support systematic risk assessment. The entropy–BP scheme combines data-driven objectivity with nonlinear correction, producing stable and interpretable indicator weights, as confirmed through robustness tests based on indicator removal and data perturbation. A real-world GIES project in East China is used as a case study. The results show clear risk grade differentiation among alternative scenarios and identify key risk drivers related to renewable energy integration, investment structure, and energy supply reliability. The proposed framework provides effective decision support for GIES project planning and risk management.