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Traffic information is collected from sensors in the urban road network, and traffic information can be said to be a mapping of people’s activities, which are difficult to model as a linear function, so this makes traffic information difficult to be predicted. In other words, traffic information is difficult to build effective models to predict traffic information because of its non-linear characteristics that are difficult to capture. As researchers go deeper, researchers have been able to extract good spatio-temporal features for modern urban road networks. However, it is worth mentioning that most researchers have neglected the importance of models for global potential features under the topology map of urban road networks, yet this global potential feature is very important for traffic prediction. In this paper, we propose a new spatio-temporal graph convolutional network model A Pagerank Self-attention Network (hereafter we abbreviate as PSN) in order to solve this problem based on a full consideration of the urban road network topology features, in which we employ a global spatio-temporal self-attention module to capture the global spatio-temporal features well. and the graph wandering module is used to propagate the spatio-temporal feature information effectively and widely. It is worth mentioning that experiments on two well-known datasets show that our proposed method achieves better prediction results compared to existing baseline methods.

With the global climate change, the frequency of extreme weather is getting higher and higher, and the threat to the safe operation of the power system is gradually increasing, which is likely to cause large-scale power outages and then result in N-k contingencies. Meanwhile, the Modular Multi-level Converter (MMC) based Multi-Terminal High Voltage Direct Current (MTDC) power system has been confirmed that can provide the possibility for the network interconnection between regional power systems and various renewable energy resources to boost supply reliability and economy. To enhance the resilience of the power grid, a generation rescheduling scheme by optimal emergency control that considers the risk-based dynamic security constraint and reactive power constraints is proposed. Based on the transient stability criterion of the rotor angle speed of the center of inertia (COI), the active power is adjusted to improve the transient stability of the system. The usefulness of the proposed control strategy is highlighted on a 10-machine-39-bus hybrid power system built on MATMTDC, a MATLAB-based open-source software. The obtained results demonstrate that the state optimization control strategy is capable of enhancing the resilience of hybrid power systems and improving the transient stability when suffering N-k contingencies.

With global warming and the depletion of fossil energy, the development of photovoltaic (PV) energy has attracted attention in many countries. However, when large-scale PV power stations are connected to the power grid through inverters, the inertia and damping capacity of the power system are greatly reduced, leading to frequent power oscillations in the power system. To solve this problem, this study proposes a control strategy for PV grid-connected inverters based on the model predictive control (MPC) algorithm. Based on the MPC algorithm and the establishment of a discrete-time predictive model, the PV grid-connected inverter dynamically adjusts its output current to suppress power oscillations. The simulation results demonstrated the effectiveness of the proposed control strategy in damping power oscillations in the power grid and enhancing the stability of the power system.

In recent years, with the continued development and popularity of sustainable energy sources and the increasing utilization of solar energy, accurate solar radiation prediction has become important. In this paper, we propose a new model based on deep learning, Feature-enhanced Gated Recurrent Unit, hereafter referred to as FEGRU, for solar radiation prediction. This model takes the source data with one-dimensional convolution and self-attention to feature attention and processes the data features, and then GRU performs feature extraction on solar irradiance data. Finally, the data dimensionality is transformed by a fully connected layer. The main advantage of FEGRU is that it does not require auxiliary data, but only time series data of solar irradiance can be used for good solar irradiance prediction. Our experiments with solar irradiance samples in Lyon, France, show that our model has better prediction results than the baseline model.

With the rapid growth of wind power penetration into modern power grids, wind speed forecasting plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the existing wind speed forecasting methods are modeled as black boxes, which are very complicated and cannot be written down explicitly due to the complex fluctuation characteristics of wind speed series. To this end, this study proposes a novel direct method based on an explainable neural network (xNN) for deterministic and probabilistic wind speed forecasting. It can theoretically extract the nonlinear mapping features in wind speed, thereby providing a clear explanation of the relationship between the input and the output of the forecasting model. Then, the uncertainties in wind speed are statistically synthesized via the kernel density estimation method. Finally, we use wind speed data from real wind farms in Belgium to verify the feasibility and effectiveness of the proposed method. The simulation results demonstrate that it is not only able to accurately extract the non-stationary feature in the wind speed series but also superior to other benchmark algorithms in prediction accuracy. Therefore, the proposed method has a high potential for practical applications in real electric power and energy systems.

Large-scale wind power integration into the power system has promoted the development of a multiterminal DC (MTDC) transmission grid with a modular multilevel converter (MMC). Basically, MTDC with MMC is a typical cyber–physical system with continuous coupling interactions between cyber assets and power systems. However, cyber events may introduce many internet-based vulnerabilities and even result in the loss of transient stability of the power system. Therefore, a voltage compensation-based two-level hierarchical adaptive control strategy is proposed in this article. At the higher level, a modified MMC output current reference calculation method is developed in the αβ framework to guarantee the transient stability of the power system, whereas a feedback adjustment method is proposed in the MMC control framework, at the bottom level, to contain the controller from deviating from its output reference while eliminating the impact of cyber communication delay on transient stability. The article shows that the proposed hierarchical control strategy can improve the transient stability of the power grid under the interference of three-phase ground faults in physical and communication delays in the cyber layer. Finally, the simulation results of the modified IEEE 9-bus test system with MMC–MTDC are used to demonstrate the effectiveness of the proposed scheme.