A Planning Method for Integrated Community Energy Systems Considering Massive Reliability Constraints

With the increasing integration of renewable energy sources, Integrated Community Energy Systems (ICES) have emerged as a promising solution for enhancing local energy resilience and sustainability. However, the spatiotemporal mismatch between multi-energy demand and supply introduces significant operational risks. To address these challenges, it is essential to incorporate load shedding-based reliability assessments into the planning phase of ICES. This paper proposes a planning framework for Integrated Community Energy Systems under massive reliability constraints. Operational uncertainties stemming from renewable generation, electricity-heat-gas interactions, and community-level demand variability are represented through a comprehensive set of potential scenarios to ensure robust reliability coverage. To alleviate the computational burden associated with large-scale scenario evaluation, a Lagrangian-based state-space classification method is developed. By identifying active reliability constraints, the proposed method effectively reduces computational complexity while preserving planning accuracy. Case studies validate that the proposed approach not only improves system reliability but also significantly enhances planning efficiency for ICES.