With large-scale wind and solar power connected to the power grid, the randomness and volatility of its output have an increasingly serious adverse impact on power grid dispatching. Aiming at the system peak shaving problem caused by regional large-scale wind power photovoltaic grid connection, a new two-stage optimal scheduling model of wind solar energy storage system considering demand response is proposed. There is a need to comprehensively consider the power generation cost of various types of power sources, day-ahead load forecasting information, and other factors and plan the day-ahead output plan of the energy storage system with the minimum system operation cost as the optimization objective of day-ahead dispatching. The demand response strategy is introduced into the time-ahead optimal scheduling, and the optimization of the output value of the energy storage system in each period is studied with the goal of minimizing the system adjustment cost. The particle swarm optimization algorithm is used to solve the model, and the IEEE33 node system is used for an example simulation. The results show that using the demand response and the collaborative effect of the energy storage system can suppress the uncertainty of wind power and photovoltaic power, improve the utilization rate of the system, reduce the power generation cost of the system, and achieve significant comprehensive benefits.

To mitigate the volatility and instability caused by large-scale wind power integration in new-type power systems, hybrid energy storage systems (HESSs) can offer effective frequency support to wind farms. This paper presents a coordinated wind storage frequency control strategy that incorporates optimal power allocation within an HESS. First, wind power output is decomposed and reconstructed into low- and high-frequency components via variational mode decomposition (VMD) optimized with the multi-verse optimization (MVO) algorithm, followed by the establishment of a PI-based HESS frequency response model. Second, an SOC-aware flexible frequency division strategy is designed by coordinating the participation sequence of the wind turbine and the HESS. The regulation process is divided into three stages, namely, wind turbine regulation, joint wind storage regulation, and HESS-dominant regulation, to suppress frequency fluctuations induced by wind power variations. Finally, primary frequency regulation performance indices are proposed and validated in a three-machine, nine-bus system. The simulation results demonstrate that the coordinated use of different storage types within the HESS enhances the grid-connected stability of the wind storage system, while the incorporation of hybrid storage improves wind power utilization.

To address current-density ramping, cell-voltage increase, and elevated operating stress in anion exchange membrane water electrolyzers (AEMWEs) under fluctuating wind and photovoltaic (PV) power inputs, this study develops a dynamic AEMWE model that couples voltage losses, thermal dynamics, and water-management states. A forecast-assisted reference governor based on short-term power prediction and dynamic constraints is further proposed. Using German Open Power System Data (OPSD) wind and PV, the effects of power-command correction on current density, voltage efficiency, specific energy consumption (SEC), voltage-limit exceedance, and a degradation-related stress proxy are analyzed. The steady-state benchmark distinguishes a separate 21-point empirical polarization fit (same-set RMSE 0.0059 V and MAPE 0.292%) from the mechanistic voltage-loss model actually used in the dynamic simulations (same-set RMSE 0.5063 V and MAPE 28.42%); consequently, the dynamic results are treated as model-conditioned rather than experimentally validated. In the 24 h case, explicit state-constrained smoothing reduced the maximum cell voltage from 2.2479 to 2.1501 V and SEC from 59.4755 to 58.1968 kWh kg⁻¹, but hydrogen yield and renewable-energy utilization decreased from 1.51997 to 0.54293 kg d⁻¹ and from 99.52% to 34.78%, respectively. The forecast envelope did not bind on the selected smooth day; across 30 representative days it reduced the stress proxy relative to the no-look-ahead state-constrained baseline on only 3–6 days depending on wind–PV composition, while in a diagnostic ramp-down case it activated 10 times and reduced cumulative ramping by 6.7% at the cost of a 16.9% hydrogen-yield loss. These results provide model-based exploratory evidence and separate the benefit of state projection from the incremental value of prediction.

This paper probes into a sensorless control method of flux-switching permanent linear magnet (FSPMLM) with high-frequency signal injection, and analyses the FSPMLM structure through salient pole verification. Specifically, a high frequency sinusoidal voltage signal was superimposed into the estimated d-axis, and the mover position was demodulated after the regulation of q-axis current, which is relative to error angle. Ultimately, the exact mover position and speed were obtained. To solve the starting problem of the FSPMLM, improved magnetic pole identification was performed based on space vector pulse width modulation for an arbitrary initial position. Through the simulation of the FSPMLM and the control system, it is confirmed that the proposed approach is of high accuracy and effectiveness.