This paper endeavors to apply a novel intelligent damping controller (NIDC) for the static synchronous compensator (STATCOM) to reduce the power fluctuations, voltage support and damping in a hybrid power multi-system. In this paper, we discuss the integration of an offshore wind farm (OWF) and a seashore wave power farm (SWPF) via a high-voltage, alternating current (HVAC) electric power transmission line that connects the STATCOM and the 12-bus hybrid power multi-system. The hybrid multi-system consists of a battery energy storage system (BESS) and a micro-turbine generation (MTG). The proposed NIDC consists of a designed proportional–integral–derivative (PID) linear controller, an adaptive critic network and a proposed functional link-based novel recurrent fuzzy neural network (FLNRFNN). Test results show that the proposed controller can achieve better damping characteristics and effectively stabilize the network under unstable conditions.

In this article, the characteristics of the wave energy converter are considered and a novel dynamic controller (NDC) for a permanent magnet synchronous generator (PMSG) is proposed for Wells turbine applications. The proposed NDC includes a recursive cerebellum model articulation controller (RCMAC) with a grey predictor and innovative particle swarm optimization (IPSO). IPSO is developed to adjust the learning speed and improve learning capability. Based on the supervised learning method, online adjustment law of RCMAC parameters is derived to ensure the system’s stability. The NDC scheme is designed to maintain a supply–demand balance between intermittent power generation and grid power supply. The proposed NDC exhibits an improved power regulation and dynamic performance of the wave energy system under various operation conditions. Furthermore, better results are obtained when the RCMAC is used with the grey predictive model method.

To build a large-scale renewable energy integrated system in the power system, power fluctuation mitigation and damping measures must be implemented during grid connection. PID damping controllers and traditional intelligent controllers with pole configuration are usually used for improving damping. Integration of large wind power plants and photovoltaic power plants into the power system faces transient power oscillation and fault ride-through (FRT) capability under fault conditions. Therefore, this paper proposes a static synchronous compensator (STATCOM) damper based on a recurrent Petri fuzzy probabilistic neural network (RPFPNN) to improve the transient stability of the power system when large offshore wind farms and photovoltaic power plants are integrated into the power system, suppress power fluctuation, and increase FRT capability. To verify the effectiveness of the proposed control scheme, a three-phase short circuit fault at the connected busbar is modeled in the time domain as part of a nonlinear model. From the comparison of simulation results, the proposed control scheme can effectively slow down the transient fluctuation of power supply to the grid-connected point when the grid is faulty, reach steady-state stability within 1–1.5 s, and reduce overshoot by more than 50%. It can also provide system voltage support at an 80% voltage drop and assist in stabilizing the system voltage to increase FRT capability. It also improves stability more than PID controllers when disturbances are present. Therefore, it maximizes the stability and safety of the power grid system.

The integration of wind energy into power systems is essential for achieving global decarbonization goals but poses significant challenges, including transmission losses, grid instability, and risks of wind farm disconnection during contingencies. This review focuses on advanced grid stability technologies, optimization strategies, and carbon trading mechanisms, proposing a synergistic framework to address these issues. By enhancing transmission efficiency and maintaining grid stability, these solutions reduce energy losses, contribute to carbon reduction, and create economic incentives through carbon credits. Moreover, optimization models enable wind farms to remain operational during severe faults, ensuring their active participation in carbon markets. This review connects recent technical advancements with economic and policy frameworks, offering a comprehensive pathway to achieving sustainable and stable power systems while maximizing the economic potential of wind energy.

To enhance the economic viability of wind energy in cold regions and ensure the safe operational management of wind farms, this paper proposes a short-term wind turbine blade icing wind power prediction method that combines principal component analysis (PCA) and fractional Lévy stable motion (fLsm). By applying supervisory control and data acquisition (SCADA) data from wind turbines experiencing icing in a mountainous area of Yunnan Province, China, the model comprehensively considers long-range dependence (LRD) and self-similar features. Adopting a combined pattern of previous-day predictions and actual measurement data, the model predicts the power under near-icing conditions, thereby enhancing the credibility and accuracy of icing forecasts. After validation and comparison with other prediction models (fBm, CNN-Attention-GRU, XGBoost), the model demonstrates a remarkable advantage in accuracy, achieving an accuracy rate and F1 score of 96.86% and 97.13%, respectively. This study proves the feasibility and wide applicability of the proposed model, providing robust data support for reducing wind turbine efficiency losses and minimizing operational risks.

As the penetration of renewable energy sources (RESs), particularly wind power, continues to rise, the uncertainty in power systems increases. This challenges traditional optimal power flow (OPF) methods. This paper proposes a Calibrated Safety Constraints Optimal Power Flow (CSCOPF) model that uses the Improved Acceleration Coefficient-Based Bee Swarm algorithm (IACBS) in combination with the equivalent current injection (ECI) model. The proposed method addresses key challenges in wind-integrated power systems by ensuring preventive safety scheduling and enabling effective power incident safety analysis (PISA). This improves system reliability and stability. This method incorporates mixed-integer programming, with continuous and discrete variables representing power outputs and control mechanisms. Detailed numerical simulations were conducted on the IEEE 30-bus test system, and the feasibility of the proposed method was further validated on the IEEE 118-bus test system. The results show that the IACBS algorithm outperforms the existing methods in both computational efficiency and robustness. It achieves lower generation costs and faster convergence times. Additionally, the CSCOPF model effectively prevents power grid disruptions during critical incidents, ensuring that wind farms remain operational within predefined safety limits, even in fault scenarios. These findings suggest that the CSCOPF model provides a reliable solution for optimizing power flow in renewable energy-integrated systems, significantly contributing to grid stability and operational safety.