Accurate short-term wind power forecasting (WPF) plays a crucial role in grid scheduling and wind power accommodation. Numerical weather prediction (NWP) wind speed is the fundamental data for short-term WPF. At present, reducing NWP wind speed forecast errors contributes to improving the accuracy of WPF from the perspective of data quality. In this article, a variational mode decomposition combined with bidirectional gated recurrent unit (VMD-BGRU) method for NWP wind speed correction and XGBoost forecasting model are proposed. First, several NWP wind speed sub-series are divided by VMD to obtain more abundant multidimensional timing features. BGRU is applied to establish the potential relation between decomposed NWP wind speed sub-series and measured wind speed and get the proposed wind speed correction model. Then, a more clear regression forecasting model is trained based on XGBoost using historical measured wind speed and power. The corrected NWP wind speed is used to forecast wind power by XGBoost. Finally, the superiority of the proposed method is validated on a wind farm located in China. The results show that the proposed correction model and forecasting model outperform other compared models.

With the increasing integration of renewable energy resources and other forms of dispersed generation, more and more variances and uncertainties are brought to modern power systems. The dynamic security assessment (DSA) of modern power systems is facing challenges in ensuring its accuracy for unpredictable operating conditions (OC). This paper proposes a novel approach that uses random forest (RF) for online DSA. Hourly scenarios are generated for the database according to the forecast errors of renewable energy resources, which are calculated from historical data. Fed with online measurement data, it is able to not only predict the security states of current OC with high accuracy, but also indicate the confidence level of the security states one minute ahead of the real time by an outlier identification method. The results of RF together with outlier identification show high accuracy in the presence of variances and uncertainties due to wind power generation. The performance of this approach is verified on the operational model of western Danish power system with around 200 transmission lines and 400 buses.

To improve the accuracy of short-term wind power prediction, a short-term wind power prediction model based on the LSTM model and multiple error correction is proposed. First, an affine wind power correction model based on assimilative migration is established to reduce the errors caused by false positives from the initial data. Then, a self-moving window LSTM prediction model based on the improved particle swarm optimization algorithm was established. By improving the particle swarm optimization algorithm, the optimal hidden neuron number and the optimal learning rate of the LSTM model were calculated to enhance the model’s accuracy. Definitively, the idea of error feedback prediction is used to correct the initial prediction error, and the prediction error is fed back to the LSTM model to reduce the error caused by the calculation of the LSTM model. By starting from the initial data error, model accuracy error, and model prediction error, multiple error correction of wind power is realized to improve the model accuracy. The simulation results show that the method improves the model’s prediction accuracy by using assimilative transfer and error feedback, contributing to the economic operation and sustainable development of the power system. Unlike traditional improvement ideas, the proposed improvement ideas do not involve the inherent characteristics of the original prediction methods. This method does not need to introduce other auxiliary methods and has good universality.

With the great increase of renewable generation as well as the DC loads in the distribution network; DC distribution technology is receiving more attention; since the DC distribution network can improve operating efficiency and power quality by reducing the energy conversion stages. This paper presents a new architecture for the medium voltage AC/DC hybrid distribution network; where the AC and DC subgrids are looped by normally closed AC soft open point (ACSOP) and DC soft open point (DCSOP); respectively. The proposed AC/DC hybrid distribution systems contain renewable generation (i.e., wind power and photovoltaic (PV) generation); energy storage systems (ESSs); soft open points (SOPs); and both AC and DC flexible demands. An energy management strategy for the hybrid system is presented based on the dynamic optimal power flow (DOPF) method. The main objective of the proposed power scheduling strategy is to minimize the operating cost and reduce the curtailment of renewable generation while meeting operational and technical constraints. The proposed approach is verified in five scenarios. The five scenarios are classified as pure AC system; hybrid AC/DC system; hybrid system with interlinking converter; hybrid system with DC flexible demand; and hybrid system with SOPs. Results show that the proposed scheduling method can successfully dispatch the controllable elements; and that the presented architecture for the AC/DC hybrid distribution system is beneficial for reducing operating cost and renewable generation curtailment.

In recent years, hybrid wind-photovoltaic (PV) systems are flourishing due to their advantages in the utilization of renewable energy. However, the accurate assessment of the maximum integration of hybrid renewable generation is problematic because of the complex uncertainties of source and demand. To address this issue, we develop a stochastic framework for the quantification of hybrid energy hosting capacity. In the proposed framework, historical data sets are adopted to represent the stochastic nature of production and demand. Moreover, extreme combinations of production and demand are introduced to avoid multiple load flow calculations. The proposed framework is conducted in the IEEE 33-bus system to evaluate both single and hybrid energy hosting capacity. The results demonstrate that the stochastic framework can provide accurate evaluations of hosting capacity while significantly reducing the computational burden. This study provides a comprehensive understanding of hybrid wind-PV hosting capacity and verifies the excellent performance of the hybrid energy system in facilitating integration and energy utilization.

As the requirements of economical operation and reliability on power grid are enhanced gradually nowadays, the existing under frequency load shedding (UFLS) scheme is not quite fit for the modern power system that integrates high wind power. In this paper, the impacts of high wind power penetration on the UFLS are discussed thoroughly. A novel adaptive load shedding (LS) scheme is presented taking the high wind power penetration into account. In the proposed scheme, the equivalent inertia constant (EIC) is calculated accurately to improve the power deficit accuracy so as to reduce the error of LS. The dynamic correction of power deficit is able to solve the negative effects of the wind power output random reduction/the wind generator tripping. Besides, the locking criterion is capable of avoiding the influences of the wind power output random increase on the LS, thus cutting down the LS costs and even preventing the frequency overshoot. Moreover, in terms of the LS parameters setting, the coordination of the low frequency protection of the wind generator and the frequency threshold is addressed. The location and capacity model of LS, which is based on the load characteristics, can ameliorate the frequency recovery process. Finally, the validity and robustness of the proposed scheme are verified in the simulations on the IEEE-39 bus system with high wind power penetration.