After comparing the economic advantages of different methods for energy storage system capacity configuration and hybrid energy storage system (HESS) over single energy storage system, a method based on improved moving average and ensemble empirical mode decomposition (EEMD) to smooth wind power fluctuations is proposed aiming at the optimal capacity configuration of HESS. Firstly, EEMD was used to decompose the HESS reference power which was derived by improved moving average filtering, and then several intrinsic mode functions (IMFs) were obtained. Each IMF instantaneous frequency-time profile was processed by Hilbert transform, the so- call gap frequency was identified, and then reconstruct the high and low frequency components to obtain the HESS reference power. Subsequently, taken the energy storage system charge-discharge efficiency and state of charge (SOC) into account, the rated power and capacities of each scheme was determined. Finally, based on Life Cycle Cost (LCC) theory, an energy storage system economic cost calculation model was established to compare the costs of each scheme and select the optimal one.

The integration of large-scale wind power and photovoltaics into the power system will aggravate the voltage fluctuation of grid nodes, while when the reactive power of new energy units participates in the optimization of reactive power and voltage of the system, it will promote the consumption of new energy while effectively improving the stable operation level of the system. In this work, the grey wolf algorithm (GWO) of entropy weight is introduced to solve the optimal compromise solution of multi-objective reactive power optimization of transmission network, and the network loss and voltage deviation and static voltage stability margin are taken as three objective functions to evaluate and make use of the reactive power regulation performance of new energy units. Specifically, the cooperative optimization of reactive power regulation performance of wind turbine and photovoltaic generating set and reactive power compensation of original power system is adopted, the efficient GWO) is incorporated to solve the multi-objective reactive power optimization model, the entropy method is introduced to obtain the optimal compromise solution of reactive power optimization, and a numerical example is analyzed by using IEEE39 transmission network system. Simulation results confirmed that the proposed multi-objective reactive power and voltage optimization model and grey wolf optimization algorithm can effectively improve the system node voltage quality and improve the stable operation level of the system.

In order to efficiently use energy storage resources while meeting the power grid primary frequency modulation requirements, an adaptive droop coefficient and SOC balance-based primary frequency modulation control strategy for energy storage is proposed. Taking the SOC of energy storage battery as the control quantity, the depth of energy storage output is adaptively adjusted to prevent the saturation or exhaustion of energy storage SOC. The balanced control strategy is introduced to realize the rational utilization of resources and the fast balance of SOC in the process of primary frequency modulation of energy storage battery under different charge states. Then, four evaluation indexes are proposed to evaluate the effect of primary frequency modulation and SOC maintenance. Taking a regional power grid as an example, a simulation analysis is carried out under step load disturbance and continuous load disturbance. According to the simulation results, the proposed control strategy is effective in power system frequency regulation and battery SOC maintenance.

In recent years, the world has been actively promoting the development of wind power, photovoltaic, and other new energy. The inherent randomness and intermittency of wind power output have led to the reduction of supply-side controllability and stability, and the power system is facing severe challenges. Aiming at the irregular fluctuation of wind power output and the restriction between the charge and discharge depth and service life of hybrid energy storage equipment, a hierarchical control strategy for a hybrid energy storage system based on improved GA-SVR wind power prediction is proposed. First of all, the short-term prediction of wind power output is carried out using Support Vector Regression (SVR), and the improved genetic algorithm is used for optimization. Then, the result obtained from the prediction calculation is used as the wind power output, and the internal initial power of each energy storage element is obtained through the hybrid energy storage capacity configuration method and further controlled through hierarchical control regulation. Finally, a simulation experiment is carried out on the proposed control strategy. The simulation algorithm shows that the proposed method can not only enhance the effective output of new energy but also extend the service life of energy storage and ensure the safe and stable operation of the power system.

With the continuous improvement of wind power penetration in the power system, the volatility and unpredictability of wind power generation have increased the burden of system frequency regulation. With its flexible control mode and fast power adjustment speed, energy storage has obvious advantages in participating in power grid frequency regulation. Therefore, this paper studies the control strategy of wind energy storage combined with primary frequency regulation and proposes a control method of wind energy storage combined with primary frequency regulation based on the operation state of wind turbines. This paper analyzes the reserve power and rotor reserve kinetic energy of wind turbines operating in different wind speed ranges, introduces the fan frequency regulation operation state coefficient to quantify the real-time frequency regulation ability of the unit, regards the fan and energy storage system as independent frequency regulation sources, and designs the control strategy of joint frequency regulation of wind and storage in different wind speed ranges. The energy storage system is employed to participate in frequency control in the low-wind-speed range, thereby addressing the “blind spot” issue of wind turbine unit frequency control alone. In the medium- and high-wind-speed ranges, the real-time complementary output of wind and energy storage power is achieved by assigning weights based on the frequency control operation status coefficient. Finally, the effectiveness of the joint frequency modulation control strategy of wind storage in low-, medium-, and high-wind-speed regions is verified in the simulation model.

Under the background of large-scale and rapid development of renewable energy, in order to improve the economic benefit of the system and ensure the reliability of the system, this paper introduces hydrogen production and energy storage into the independent wind/photovoltaic/hydrogen/storage capacity optimization configuration method. In order to minimize the total planning cost and self-sufficiency rate of system power supply, a capacity optimization allocation model was established. On the basis of this model, the Multi-objective Salp Swarm Algorithm (MOSSA) was improved in Algorithm structure, Tent chaotic mapping was introduced to initialize the population, and the positions of leaders and followers were updated based on the adaptive spiral search strategy. The validity of the algorithm is verified by test function. Finally, the specific example is solved by MATLAB programming, and the improved multi-objective Salp Swarm Algorithm (IMOSSA) is used to obtain the capacity configuration scheme, which provides reference for the optimization design of independent photovoltaic hydrogen storage system.