This study explores the smoothing effect on the uncertainty of the wind power fluctuations that affect a power system at points of common coupling. Set pair analysis is applied to evaluate the similarity between the power fluctuations of a single wind turbine and those of all aggregated wind turbines, based on which a quantitative index describing the wind power smoothing effect is proposed. The smoothing effect characteristics of a wind farm cluster are investigated for different numbers of wind turbines, different wind speeds, different seasons and multiple sampling intervals. A significant smoothing effect is usually observed at a shorter sampling interval and a higher wind speed, and the smoothing effect index increases with an increase in the numbers of wind turbines and farms. The experimental results indicate that the wind power forecast accuracy varies with the smoothing effect index, which is influenced by the number of wind farms. Furthermore, the aggregated output from a wind farm cluster with a higher smoothing effect index exhibits better forecasting performance than that from a single wind farm, showing that the trend of the wind power series becomes smoother due to the smoothing effect, thus enabling 1-step-ahead wind power forecasting with higher accuracy

As the low-carbon economy continues to expand, wind power, as one form of clean energy, promotes the low-carbon power development process. In this paper, a multi-objective environmental economic dispatch (EED) model is proposed considering multiple uncertainties of the system. Carbon trading costs and green certificate trading costs are introduced into the economic costs. Meanwhile, the objective function of pollutant emissions is taken into account in the model, which can further promote the reduction of pollutant emissions in the system scheduling. The output of wind turbines is uncertain and volatile, so it brings new challenges to the power system EED once the large-scale wind power accesses the power grid. For the multiple uncertainties of the system, fuzzy chance-constrained programming is introduced, and the output of the wind turbines and the load are regarded as fuzzy variables. We use the clear equivalence forms to clarify the fuzzy chance constraints. The improved multi-objective standard particle swarm optimization (SPSO) algorithm is used to solve the optimization problem effectively. The feasibility and effectiveness of the proposed model and algorithm are verified by an example of a 10-unit system with two wind farms.

With the large-scale integration of renewable energy through power electronic inverters, modern power systems are gradually transitioning to low-inertia systems. Grid-forming inverters are prone to power overshoot and frequency deviation when facing external disturbances, threatening system stability. Existing methods face two main challenges in dealing with complex disturbances: neural-network-based approaches have high computational burdens and long response times, while traditional linear algorithms lack sufficient precision in adjustment, leading to inadequate system response accuracy and stability. This paper proposes an innovative coordinated adaptive control strategy for virtual inertia and damping. The strategy utilizes a Radial Basis Function neural network for the adaptive regulation of virtual inertia, while the damping coefficient is adjusted using a linear algorithm. This approach provides refined inertia regulation while maintaining computational efficiency, optimizing the rate of change in frequency and frequency deviation. Simulation results demonstrate that the proposed control strategy significantly outperforms traditional methods in improving system performance. In the active power reference variation scenario, frequency overshoot is reduced by 65.4%, active power overshoot decreases by 66.7%, and the system recovery time is shortened. In the load variation scenario, frequency overshoot is reduced by approximately 3.6%, and the maximum frequency deviation is reduced by approximately 26.9%. In the composite disturbance scenario, the frequency peak is reduced by approximately 0.1 Hz, the maximum frequency deviation decreases by 35%, and the power response improves by 23.3%. These results indicate that the proposed method offers significant advantages in enhancing system dynamic response, frequency stability, and power overshoot suppression, demonstrating its substantial potential for practical applications.