Accelerating the transformation of energy structure and promoting the grid connection of renewable energy power generation is an important initiative to address climate change and the development of renewable energy. Energy storage technology can improve the stability of power grid and enhance the utilization rate of renewable energy. Among the energy storage technologies, compressed air energy storage has been widely studied for its high efficiency, low investment cost and environmental friendliness. Compared with the conventional constant-capacity compressed air energy storage technology, isobaric compressed air energy storage avoids the unavoidable buffer air in the constant-capacity compressed air energy storage system, enables the compressor and expander to operate efficiently at constant discharge pressure, and eliminates the throttling loss in front of the expander unit. The advantages of isobaric compressed air energy storage technologies are introduced, and the isobaric compressed air energy storage technologies are classified into underwater compressed air energy storage, pumping-compensated compressed air energy storage, solid-compensated compressed air energy storage, and gas-phase-change-compensated compressed air energy storage. Moreover, the basic principles, research progress and challenges of the above four types of isobaric compressed air energy storage technologies are discussed. Finally, the development of the isobaric compressed air energy storage technologies is prospected.

A wind-drove compressed air energy storage (W-CAES) system is proposed, its main advantage is that it can reduce the waste of wind energy caused by the fluctuation and randomness of wind energy. The direct-driven compressor of wind turbine gets rid of the dependence of compressor on the input electricity, which is more suitable for off-grid power generation system. The model of the W-CAES system is established, the parameters of the wind turbine direct-drive compressed air energy storage system are designed, and the effects of wind speed, ambient temperature, and air humidity on efficiency of the system are analyzed. The results show that, the filling time increases with the decrease of wind speed with the same storage volume, and the filling times are 0.71 h and 1.64 h when the wind speeds are 14 m/s and 6 m/s, respectively. The system efficiency decreases slightly with the increase of ambient temperature and air humidity. When the ambient temperatures are -30 ℃ and 40 ℃, the corresponding system efficiencies are 52.97% and 52.08%, respectively. When the relative humidity of the air is 0 and 1, the corresponding system efficiencies are 52.27% and 52.14%, respectively.

In order to explore the type of underground cavern with compressed air energy storage from the perspective of thermodynamics, a numerical model of the first inflation and pressurization process of the cavern considering turbulence, heat transfer and real air characteristics is established, by using the computational fluid dynamics (CFD) method. The effects of different length-diameter ratios and inlet diameters of inflatable pipes on temperature rise of gas and lining materials in the cavern and the temperature distribution in the cavern are studied, and the control measures are put forward for the local high temperature phenomenon in the cavern. The main conclusions are as follows. When the length-diameter ratio is small (large tank gas storage), the temperature distribution in the cavern is relatively uniform. With the increase of the ratio of length to diameter (tunnel-type gas storage), the temperature distribution in the cavern appears stratification phenomenon, and the extremely high temperature zone appears at the end of the cavern (stuffy top effect). The temperature rise of the steel plate sealing layer is the largest in the process of inflation and pressurization of the cavern, the temperature change of the concrete lining is small, and the surrounding rock is almost not affected by temperature change in the cavern. Reducing the inlet diameter of the inflatable pipe can reduce the temperature in the cavern to a certain extent and promote the outward heat transfer. For the annular tunnel type cavern, the proposed improved inflation method can make the temperature distribution in the cavern uniform, avoid the stuffy roof effect, and provide a useful reference for engineering design.

As a large scale of physical energy storage technology, compressed air energy storage technology is widely used in consumption of renewable energy and peak shaving of power grids. A compressed air energy storage system coupled with molten salt thermal storage is designed, and the composite system is modeled using Ebsilon software. Based on the operating conditions of the energy storage system supplying hot water, steam, and electricity, the exergy efficiency, thermal efficiency, and economic performance under different operating modes are studied. The results indicate that, the composite system achieves the highest exergy efficiency (64.98%) at a storage pressure of 7 MPa and an exhaust temperature coefficient of 1.96. The highest thermal efficiency (91.55%) is attained at a storage pressure of 12 MPa. In the application scenario of combined heat, steam, and electricity cogeneration, the optimal energy storage duration is 6 hours. Additionally, at gas storage pressures of 7 MPa and 12 MPa, the optimal power generation durations are 6 hours and 8 hours. This research provides theoretical guidance for the study of cogeneration of power and heating using compressed air energy storage system coupled with molten salt thermal storage system.

A double-layer optimization site selection method for energy storage with grid-forming demand in novel power system is proposed, which considers the response of energy storage to peak shaving and frequency regulation in the power system and establishes a multi-objective double-layer optimization model. The operation layer counts the wind and solar power abandonment and network losses into the economic penalty, and takes the optimal annual operating cost of the system as the objective, considers the benefits of peak shaving and frequency modulation. The planning layer evaluates the security of the system and models the system by taking the optimal comprehensive annual operating cost of the system as the objective. Simulation and analysis of the algorithms are carried out using the improved empire competition algorithm. The peaking and frequency regulation economics and energy storage siting in the optimal scenario are illustrated through multi-scenario comparisons. Finally, the IEEE-33 node arithmetic system is simulated and analyzed to verify the validity of the proposed model. Furthermore, uncertainty factor indicators are selected to conduct sensitivity analysis on total costs, and the indicators that need more attention to affect economic costs are determined.

A combined reactive power control strategy for permanent magnet direct-drive wind turbine and distributed hybrid energy storage system is proposed. Firstly, the reactive power regulation capability of the permanent magnet direct-drive wind turbine and energy storage system is analyzed, and it is determined that both the wind turbine and energy storage system can participate in reactive power regulation through converter control. Secondly, the reactive power control strategy is put forward, which is presented in terms of signal reception, initial allocation, and internal allocation. In initial allocation, the equal margin allocation method is adopted. In internal allocation, the proportional allocation with the priority output of energy storage is considered. Finally, the effectiveness of the strategy is verified by simulation, it shows that the power grid voltage can be supported by fully utilizing the reactive capacity of the wind turbine and energy storage system.

Virtual Synchronous Generator (VSG), as one of the primary technologies in grid-forming controls, provides inertia support to the power grid. However, due to the limited capacity of individual converters, when larger inertia support is required, multiple VSGs must run in parallel, making the coordinated control of multiple VSGs a subject of significant interest. In this regard, a state-space model for multiple VSGs in parallel is established, and the system stability is analyzed through the eigenvalues of the state variable matrix. Concurrently, a coordinated control strategy for multiple VSGs based on model predictive control is proposed, which introduces the angular frequency deviation and power angle difference as performance indicators to design the objective function. The optimal active power increment required is solved, and the output angular frequency is dynamically adjusted through the power-frequency coefficient, enabling active support for the output frequency and effectively suppressing system frequency fluctuations caused by VSG paralleling, thus the grid stability is enhanced. The results indicate that, compared with the conventional VSG paralleling systems, the proposed MPC-VSG parallel control method can shorten the transient response time of the system and improve its robustness under transient conditions. The simulation result confirms the effectiveness of the proposed approach.

Against the conflict between carbon emission and operation cost in integrated energy systems, a multi-objective optimal scheduling method for wind-solar-thermal-storage integrated energy system considering carbon capture is proposed. It explores how carbon capture equipment affects the renewable energy consumption, carbon emissions, and operating costs. Taking the electric load data of a typical day in a specific area as a reference and the improved IEEE 30-bus system as the example, the system economy is optimized. The results show that, compared with the wind-solar-thermal and wind-solar-thermal-storage scenarios without carbon capture, the operating costs of the integrated energy system considering carbon capture reduces by 5.19% and 2.86% respectively on typical days, and the carbon emissions decrease by 1 159 t and 1 013 t, respectively. The consumption rate of wind and solar power generation increases by 5.01% and 2.82%, respectively. Moreover, with the minimum system operation cost and carbon emissions as the optimization objectives, the non-dominated sorting genetic algorithm II is used for multi-objective optimization, and the system scheduling optimization scheme under different target weights is obtained by combining with the linear weighted sum method. The study finds that, increasing the weight of carbon emission target reduces the carbon emissions but raises the system operation costs and the cost per unit of carbon emission reduction. Specifically, when the target weight of carbon emissions rises from 0 to 0.5, the carbon emissions decrease by 5 159 t and the operating costs increase by 205 466 yuan. The carbon emission reduction cost per unit increases the least when the target weight shifts from 0.4 to 0.5. The most significant emission reductions occur when the target weight is within [0.2, 0.4]. The multi-objective optimal scheduling method considering carbon capture proposed above provides a reference for decision makers when weighing system carbon emissions and operating economy.

The large-scale integration of wind power into grid makes it difficult to sustain the peak regulation resources of the existing system, and the wind power consumption is hindered. Therefore, considering the uncertainty of wind power output and electricity price, it proposes a distribution robust optimization method for deep peak regulation of electrolytic aluminum load cooperating with thermal power and energy storage system based on Wasserstein distance. Firstly, combined with the load characteristics of electrolytic aluminum, considering the optimization of deep peak regulation capacity of the energy storage auxiliary thermal power units, an electric power system optimization framework for deep peak shaving of the electrolytic aluminum load and thermal power-energy storage system is established. Secondly, drawing on the idea of the robust model of Wasserstein distance distribution, the Wasserstein fuzzy set constraint of the purchase and sale price of the upper power grid and the output of renewable energy is constructed, and the distribution robust optimization model for deep peak regulation of the electrolytic aluminum load and thermal power-energy storage system is designed. Finally, simulation is performed to verify that the proposed method can effectively improve the peak regulation pressure, reduce the operating cost of the system, and promote the consumption of wind power. The economics and robustness of the method are verified by comparative analysis.

The stability and cost-effectiveness of power supply has been a pressing issue in areas such as isolated islands where power resources are relatively scarce and natural resources is abundant. Conventional stand-alone microgrids mostly rely on the non-dominated sorting genetic algorithm (NSGA-II) for capacity allocation, which has slightly insufficient local search capability when dealing with multi-objective optimization problems with real loads. In order to overcome this limitation, the improved strength Pareto evolutionary algorithm (SPEA2) is used to optimize the capacity allocation of wind-PV-diesel-battery stand-alone microgrid, which takes the economic cost, loss-of-load probability, and carbon emission as the optimization objectives, to achieve a more comprehensive and efficient capacity allocation. By importing the weather and load data of an isolated island and generating the real Pareto frontier of the independent microgrid with wind, PV, diesel and storage, the analysis results of SPEA2 are compared with that of multi-objective search based on indicator selection (IBEA) and NSGA-II algorithms. Compared with the NSGA-II algorithm, the anti generational distance evaluation IGD index of the SPEA2 increases by 46.83%, the spatial evaluation method Spacing index rises by 60.28%, and the real Pareto coverage CPF index grows by 35.14%, indicating the SPEA2 shows a more excellent performance. Finally, the parameters of each part are reasonably configured according to the results of capacity optimization. It shows that the joint output meets the load demand, which provides a new way of thinking for the energy management of isolated islands and other areas with scarce power resources, and also provides a valuable reference for the optimal design of multi-energy microgrids.