To address the insufficient resilience of distribution networks under high penetration of renewable energy, this paper proposes a resilience enhancement strategy incorporating advanced adiabatic compressed air energy storage (AA-CAES). A dispatch model is formulated in which AA-CAES participates in grid contingency response, and the uncertainty of renewable generation is characterized using distributionally robust chance constraints based on the Wasserstein distance. Simulation tests are conducted on a modified IEEE 33-node system to validate the effectiveness of the proposed strategy. Results show that, with AA-CAES deployed, the loss-of-load rate during extreme events is significantly reduced—decreasing by 3.84% compared to the scenario without AA-CAES, at the cost of only a 1.17% increase in dispatch cost. The study concludes that the proposed strategy effectively enhances the power supply capability of distribution networks under disaster-induced disturbances, achieving coordinated optimization between operational economy and resilience through a modest cost increment and substantial reliability improvement.

As an extension of the heat exchanger network, the array-type heat exchangers can effectively enhance the operational capability of advanced adiabatic compressed air energy storage (AA-CAES). However, the complexity of the variable-configuration array-type heat exchanger network exerts a significant influence on the operational capability of the AA-CAES system. To address this gap, this paper proposes a wide-range operational strategy for AA-CAES systems that incorporates array-type heat exchangers. First, a model of the array-type heat exchangers array for AA-CAES is established based on the thermal-electrical analogy theory. Subsequently, a wide-range operation method for AA-CAES is proposed, leveraging the operational characteristics of the array-type heat exchangers. This method determines the number of heat exchanger units participating in power regulation according to the required power output, followed by a multi-objective optimization of the array-type heat exchangers using power deviation and residual thermal energy of the thermal oil as objective functions. Finally, a case study based on the parameters of a commercially operational AA-CAES station is conducted to validate the effectiveness of the proposed method. The results demonstrate that, compared to traditional heat exchangers, the modular heat exchanger array can effectively expand the feasible operating region of the AA-CAES discharging system, reduce power tracking deviation, and increase the utilization rate of thermal energy in the thermal oil. The research will provide the theoretical foundation and technical support for flexible regulation of AA-CAES.

Underwater compressed air energy storage (UWCAES) is vital for balancing power supply-demand fluctuations but faces challenges of instantaneous overpressure and pressure oscillations in flexible balloons during deepsea operation and dynamic charging/discharging. This paper proposes a fuzzy PID (proportional integral derivative) - based method to suppress these pressure fluctuations. First, a dynamic pressure transmission model incorporating underwater environmental parameters is established for the balloon. Then, a fuzzy PID control algorithm is developed, utilizing the pressure error and its rate of change as inputs. This algorithm constructs membership functions and a fuzzy rule base to dynamically adjust PID parameters in real-time, optimizing the valve opening adjustment rate. Finally, case studies confirm algorithm robustness under dynamic conditions like vortex-induced shock. By achieving a 26.7% reduction in pressure standard deviation (to 30.8 kPa) over PID control, the proposed strategy effectively mitigates overpressure and fluctuations, advancing the deployment of underwater flexible compressed air energy storage.

With the implementation of the “dual carbon” strategic goals, the proportion of offshore renewable energy is gradually increasing, raising higher demands for the integration of renewable energy in coastal power systems. In this context, underwater compressed air energy storage (UWCAES) has emerged as one of the key technologies to address the challenges of high proportions of renewable energy in coastal areas, due to its advantages such as large capacity, zero carbon emissions, and stable operating conditions. This paper proposes a configuration strategy for UWCAES considering multi-level gas storage arrangements. Firstly, based on the spatial distribution characteristics of gas storage in shallow and deep underwater areas, a multi-level compressed air energy storage model is established to enhance the operational flexibility of UWCAES. Secondly, aiming to maximize system benefits, a configuration model for multi-level compressed air storage is proposed, which takes into account constraints related to the operation of multi-level compressed air and system power balance. Subsequently, a genetic algorithm is employed to determine the depth and capacity of gas storage in both shallow and deep water areas, facilitating rapid acquisition of configuration results. Finally, simulation cases validate the effectiveness of the proposed configuration strategy. Compared to UWCAES operating at a single gas storage pressure level, the proposed multi-level UWCAES significantly improves the grid’s capability for renewable energy absorption and economic performance. The multi-level gas storage arrangement effectively enhances the regulation performance and economic advantages of UWCAES under complex operating conditions, and provides a practical technical path for the storage planning of coastal power systems with high proportion of renewable energy.

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.