With the large-scale integration of high-penetration renewable energy into the power grid, there are increasing demands for frequency regulation. To address the issues of high regulation losses and poor economic performance resulting from the frequent ramping of conventional thermal power units, this paper proposes a secondary frequency regulation strategy for a hybrid energy storage system (HESS) that incorporates the response characteristics of both thermal power and compressed air energy storage (CAES). First, the automatic generation control signal is decomposed into high-frequency and low-frequency components using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and multiscale permutation entropy (MPE) methods. Subsequently, leveraging the similarity between thermal power units and CAES in terms of dynamic response time and regulation inertia, a coordinated control method for a thermal-HESS is developed. This method enables the rational allocation of high- and low-frequency components among different units, thereby enhancing the system’s frequency regulation performance while reducing the output variability of the thermal unit. Finally, a dynamic simulation model is built in Matlab/Simulink to validate the regulation performance and economic benefits of the proposed strategy. Simulation results demonstrate that the proposed strategy can fully leverage the analogous response characteristics between thermal power and CAES during secondary frequency regulation, as well as the complementary advantages of the HESS in terms of fast response and large capacity. This coordinated approach effectively reduces and smoothens the output of the thermal power unit, thereby enhancing the overall frequency regulation performance and economic benefits of the thermal-HESS.

Underwater compressed air energy storage (UW-CAES), which utilizes flexible underwater air bags to enable constant-pressure charge and discharge, has emerged as a compelling solution for renewable energy accommodation. However, there remains a distinct lack of research focused on parameter optimization to simultaneously reduce the capital costs of UW-CAES and enhance the operational economics of the plant. To address this critical gap, this paper proposes an optimal configuration method for UW-CAES based on distributionally robust chance constraints (DRCC). First, a comprehensive UW-CAES system model is established, explicitly accounting for the impact of pipeline pressure losses on system dynamics. Subsequently, an optimal configuration framework incorporating these pressure losses is formulated to optimize key system parameters, with the dual objectives of minimizing investment costs and maximizing operational revenues. Furthermore, the DRCC approach is employed to reformulate the stochastic chance constraints into tractable linear constraints. This mathematical transformation not only ensures computational efficiency but also facilitates a flexible trade-off between economic optimality and robustness. Case studies demonstrate the efficacy of the proposed methodology: the optimized system maintains a rated discharge power of 60 MW while reducing the required rated charge power to 53.2 MW—an 8.75% decrease compared to the original baseline—thereby significantly improving overall system efficiency. Finally, sensitivity analyses reveal that systematically calibrating the confidence level and Wasserstein radius within the DRCC framework effectively navigates the equilibrium between economic performance and system conservatism.

With the continuous increase in the scale of new energy installations and their grid integration, the inherent randomness and volatility of new sources exacerbate grid frequency deviations and increase regulation pressure, posing a serious threat to system stability, security, and economic operation. To address this issue, this paper proposes a capacity optimization configuration strategy for hybrid energy storage systems (HESSs) that accounts for energy storage response characteristics and wind power fluctuation smoothing requirements. The method employs a HESS composed of advanced adiabatic compressed air energy storage (AA-CAES) and electrochemical energy storage. First, the input power of the HESS is decomposed using variational mode decomposition (VMD). To reduce the impact of mode mixing on the accuracy of power decomposition, the parameters of the VMD algorithm are optimized using a differential evolution (DE) algorithm. Next, based on the response speed of AA-CAES, preliminary allocation boundaries are defined. Further, a secondary allocation of the hybrid energy storage power is performed with the goal of minimizing the comprehensive cost of the system. Finally, the proposed method is validated through case simulations. The results show that the proposed method reduces mode mixing during power decomposition, achieves reasonable power allocation among different energy storage systems, leverages the operational characteristics of various energy storage components, smooths wind power fluctuations, optimizes the capacity configuration of the HESS, and enhances the economic efficiency.

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.

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.

Advanced adiabatic compressed air energy storage (AA-CAES) can improve the rate of new energy consumption, and it is a key technology for new power systems. Since the compressor of the AA-CAES system adopts a centrifugal compressor, there is a phenomenon of surge and blockage during the operation, which seriously affects the safe operation of the system. In this paper, the safety control strategy of the compression side of the AA-CAES system is investigated. Firstly, a simple judgement method of the surge and blockage phenomena based on the slope of the compressor mass flow rate is proposed, and the range of the compressor's allowable mass flow rate of air flowing through the compressor at a given rotational speed is determined. Then, the anti-surge and blockage control strategy of the compression subsystem is designed to limit the range of compressor air mass flow rate by controlling the angle of the inlet guide vane of the compressor using the variable flow method. Finally, simulations are carried out under the start-stop condition and grid-connected operation condition to verify the effectiveness of the control strategy.

Advanced adiabatic compressed air energy storage (AA-CAES), with its dual capability for electricity-heat cogeneration and energy storage, offers significant potential as an energy hub for integrated electricity and heat systems (IEHS). While synergies in the electricity-heat market are known to enhance economic efficiency, it is hard to achieve cooperative operation due to the inherent differences among participants of IEHS and the absence of an incentive-compatible mechanism. To address this challenge, this paper proposes a Nash bargaining-based cooperative operation strategy for IEHS with AA-CAES. First, a cooperative alliance framework based on the Nash bargaining is proposed to optimize energy trading. Second, to overcome computational complexity, the non-convex, nonlinear Nash bargaining problem is decomposed into a two-stage optimization approach. In the first stage, a joint planning model maximizes the total profit of the alliance, determining the optimal energy interaction for each participant. In the second stage, a subsequent model ensures fair profit distribution by optimizing pricing and benefit-sharing mechanisms. Subsequently, a distributed solution strategy based on the self-adaptive alternating direction method of multipliers is utilized to preserve operator privacy and improve computational efficiency. Finally, case studies demonstrate that within the electricity-heat co-supply mode, the daily profit of AA-CAES can improve by approximately 4137.45 CNY. Meanwhile, through the proposed cooperative strategy, participants in the IEHS can obtain greater profits, which validates the effectiveness of this strategy.