To address the challenges of power fluctuations and ramping demands faced by regional integrated energy systems under high penetration of renewable energy, this paper focuses on the ramping support capability of advanced adiabatic compressed air energy storage (AA-CAES). A multi-timescale optimization dispatch model for regional integrated energy systems incorporating AA-CAES ramping capability is established. First, an operational model of AA-CAES is established to analyze its support capability for thermal power ramping. Second, a multi-timescale optimization dispatch strategy for regional integrated energy systems incorporating AA-CAES ramping capability is proposed. Long-timescale optimization minimizes operational costs while ensuring system power balance, and short-timescale dynamic power correction is achieved using model predictive control. Simulation results demonstrate that multi-timescale scheduling, incorporating AA-CAES ramping capability, effectively enhances the system’s resilience to renewable energy fluctuations, reduces thermal power dispatch requirements, lowers operational costs, and improves the integration of renewable energy. This approach provides theoretical guidance for the economic and stable operation of regional integrated energy systems.

The coupling of power and heating systems can promote renewable energy integration and improve the comprehensive efficiency of the energy system. Advanced adiabatic compressed air energy storage (AA-CAES) is a large-scale clean energy storage technology with the potential for multi-energy co-storage and supply, which can serve as an energy hub integrating power and heating systems. However, the current bidding mechanism for AA-CAES participating in electricity and heating markets as an independent entity remains unclear, and traditional modeling mostly adopts battery-like energy storage models, leading to difficulties in accurately measuring economic benefits. To address this, this paper proposes a leader-follower game-based bidding strategy for AA-CAES considering combined heat and power supply. Firstly, a combined heat and power mathematical model of AA-CAES is established by accounting for the operational characteristics of each component. Secondly, a single-leader-dual-followers leader-follower game framework is constructed, where the upper layer optimizes bidding parameters with the goal of maximizing AA-CAES’s profit, and the lower layer achieves market clearing with the objective of maximizing social welfare. To solve the challenge of solving the bi-level nonlinear model, the Karush-Kuhn-Tucker (KKT) optimality conditions and binary expansion linearization method are adopted to convert it into a single-level mixed-integer programming problem. Finally, case simulations show that AA-CAES’s profit from participating in both markets increases by 30.6% compared with participating only in the electricity market. The parameters of its own components have a significant impact on profits—especially a 10% improvement in the isentropic efficiency of the turbine can increase total profits by 28%. This study provides key references for the market operation and parameter optimization of AA-CAES.

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.