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.
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 deep-sea 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.
Driven by the “carbon neutrality and carbon peaking” goal，gravity energy storage has become an important support technology for new power systems due to its advantages of environmental protection，no self-discharge and flexible layout. Aiming at the power fluctuation problem caused by mass block scheduling in the charging and discharging process of gravity energy storage and the active/reactive power response demand of the grid，this study takes the ramp gravity energy storage as the object，and respectively constructes the simulation model of the gravity energy storage system including two types of motors（electrically excited synchronous motor and doubly-fed motor）and three typical control strategies（vector control，direct power control and sliding mode control）. The corresponding mathematical model and power coordination control strategy are established. The simulation results show that the electrically excited synchronous motor system with sliding mode control has the best dynamic response and steady-state accuracy in terms of active/reactive power regulation performance. The doubly-fed motor combined with sliding mode direct power control strategy also shows good adjustment ability and robustness.
Aiming at the problem of control interference and equipment loss caused by high frequency power electronic switching action when reconfigurable battery energy storage system participates in the frequency modulation process of power grid， a frequency modulation control strategy based on coordinated topology structure is proposed. Firstly， the operation control method of the reconfigurable battery energy storage system is designed to improve the cycle service life， flexibility and security of the battery energy storage system. Secondly， the virtual synchronous generator control is used to provide frequency modulation service. In order to reduce the influence of high-frequency power electronic switching， a reconfigurable battery energy storage system is proposed to participate in frequency modulation control strategy to ensure frequency stability. Finally， the effectiveness of the proposed control strategy is verified by simulation modeling.
To address the prominent issues of insufficient utilization of user-side flexibility resources and the low degree of energy coupling in park-level electricity-heat-hydrogen integrated energy systems，this paper proposes a low-carbon scheduling strategy incorporating the concept of equivalent energy storage. First，user-side adjustable resources are considered，and the dispersed regulation capabilities among multiple user-side entities are aggregated，thereby introducing the concept of equivalent energy storage（EES）. Second，a multi-mode coordinated operation framework is established for park-level multi-energy systems，which integrates electrical energy storage，hydrogen energy storage，and hydrogen-blended gas combined heat and power units. This framework characterizes the coupling relationships of electricity-heat-hydrogen energy flows，while a stepwise carbon trading mechanism is introduced. Together with EES，user-side adjustable resources are aggregated to reduce the system’s dependence on high-carbon units. Finally，case studies are conducted to validate the effectiveness of the proposed strategy. The results demonstrate that，compared with the case without EES，the proposed method reduces the total operating cost of the system by 13.04% and achieves a 29.62% reduction in carbon emissions under the constraints
Shared energy storage can effectively address the issues of low utilization and high costs caused by individual energy storage configurations by regulating resources across multiple regions. To further exploit the potential of shared energy storage in demand-side resources，this paper introduces electric vehicles and ice storage air conditioning，both with flexible energy storage characteristics，to construct a generalized shared energy storage model for the coordinated optimization of energy usage in smart building clusters. In response to the uncertainty of photovoltaic（PV）output on the energy input side，a time generative adversarial networks（TimeGAN）is employed to simulate a large number of PV output scenarios. By combining daily irradiance data，the static and dynamic features of these scenarios are mined，and typical scenarios are identified using K-medoids clustering. Additionally，a tiered carbon trading mechanism is introduced to limit the carbon emissions of the energy system. An optimization scheduling model for smart buildings is established，considering operational costs，carbon emissions，and user comfort，and is solved using CPLEX. Case studies demonstrate that the proposed method can generate high-quality PV output scenarios，improve regional PV consumption rates，and effectively balance user comfort and costs.
To address the issue that distributed energy storage is difficult to meet the online real-time dispatching requirements of aggregators due to its large quantity，geographical dispersion，and strong uncertainty in responding to the demands of multiple user entities，a clustering-based aggregation optimization dispatching strategy for distributed energy storage is proposed. Firstly，the holographic state model of energy storage is established by considering the physical state parameters and electrical location information of energy storage，based on which the energy storage is clustered by K-Means++ algorithm. Secondly，the frequency modulation，peak regulation，distributed energy trading and voltage regulation demands of multi-user subjects are constructed，and the corresponding service consolidated indicators of energy storage clusters are designed to determine the collection of energy storage with excellent performance to be optimised for each demand. Subsequently，taking into account the revenue of the aggregator participating in demand response of multi-user subjects and the cost of leasing energy storage，the energy storage to be optimised is optimally scheduled with the objective of optimal economic benefit of the aggregator. Finally，the feasibility of this paper’s optimal scheduling strategy for aggregation is verified through a simulation experiment.
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.
To optimize the operation of shared energy storage，this study investigates the non-cooperative game problem in transactions between shared energy storage and multi-prosumer. First，a bi-level optimization model is established to characterize the non-cooperative game relationship among the participants，aiming to optimize the trading strategies of the shared energy storage operator and the prosumers. The upper-level model maximizes the operator’s profit by optimizing its operational schedule and pricing strategy to provide charging and discharging services. The lower-level model responds to these prices by minimizing each prosumer’s operational cost through optimizing their electricity trading and storage schedules. This approach helps the operator optimize trading strategies and enhance both market competitiveness and profitability. Next，the Karush-Kuhn-Tucker（KKT）conditions are applied to transform the bi-level problem into a single-level model. The reformulated model is linearized using the big-M method and then solved numerically. Finally，simulation results demonstrate that the proposed method effectively balances the interests of both the shared energy storage operator and the prosumers，achieving a mutually beneficial outcome.
Compared with salt caverns and artificial cavities，using pipeline steel as above-ground gas storage chambers offers greater advantages for small-scale distributed compressed air energy storage（CAES）systems. This paper establishes a detailed dynamic simulation model of a 10 MW-class distributed CAES system based on AMESIM software. The research investigates key parameters such as discharge duration，above-ground storage chamber volume，system efficiency，and energy storage density under different energy storage durations and different maximum storage pressures of the above-ground storage chambers. In addition，an economic analysis of the system is also conducted. The results show that heat loss of the thermal storage and exchange system is the main cause of energy loss in the CAES system. As the energy storage duration increases，the volume of above-ground storage chambers increases，while the system efficiency remains unchanged and energy storage density increases，meanwhile，the reduction rate of the static payback period gradually slows down. With an increase of maximum ground chamber pressure，the chamber volume decreases，system efficiency declines and energy storage density increases，while the static payback period first declines and then rises. When the maximum pressure of the above-ground chamber rises from 9 MPa to 14 MPa，the system efficiency drops from 67.59% to 54.37%. The minimum static payback period of 8.29 years is achieved at the optimal pressure of 11.8 MPa.
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