Capacity Configuration Strategy for Advanced Adiabatic Compressed Air Energy Storage Based on Chance Constraints

High-penetration renewable energy systems exhibit pronounced uncertainty. As an emerging long-duration physical energy storage technology, advanced adiabatic compressed air energy storage (AA-CAES) provides valuable support for enhancing system flexibility and regulation capability. However, conventional robust planning typically adopts conservative configurations across all scenarios, making it difficult to accurately characterize the risk of power and energy limit violations in storage operation. To address this gap, this study proposes an AA-CAES capacity optimization method that incorporates wind-photovoltaic uncertainty and achieves an effective trade-off between economic performance and operational risk through chance constraints. First, a chance-constrained model is developed to bound the violation probabilities of AA-CAES charging/discharging power and energy capacity at prescribed confidence levels, and binary variables combined with a big-M linearization strategy are employed to reformulate the problem as a mixed-integer linear program (MILP). Second, a multi-scenario stochastic planning framework is constructed to represent the temporal variability of renewable resources. Finally, simulation studies and confidence-level sensitivity analyses are conducted. The results demonstrate that, compared with stochastic planning without chance constraints, the proposed method effectively controls violation risk while maintaining superior system cost performance, thereby enhancing both reliability and economic efficiency.