Applying high-reliability and low-latency scenarios of the fifth-generation mobile communication technology (5G) to large-capacity and low-latency services in the power system requires meeting the communication reliability requirements specified by the power system. However, reliability metrics based on packet loss rate fail to capture the time-dependent characteristic of time-varying wireless channels and the healthy transmission duration required for different ultra-reliable and low-latency communication (URLLC) services. Therefore, to analyze the performance of channels within a given time interval, this study employs survival analysis methods to combine URLLC key technologies with failure rate functions in reliability theory. By utilizing the received signal strength (RSS), theoretical distribution models and data-driven models are established to predict the channel reliability within the next subframe. A case study is conducted by using of the UMa-NLOS-CDL channel model to compare and analyze the proposed methods. Furthermore, considering different rain fading conditions, the reliability of multiple-input multiple-output (MIMO) and multiple-input single-output (MISO) channel systems is compared and analyzed

When acting as the shared energy storage and participating in electricity market services, the schedulable capacity that shared electric vehicle (shared EV) will provide to the grid needs in future time needs to be predicted accurately. This research first proposes a method to construct a schedulable capacity dataset for shared EVs based on publicly available shared vehicle rental service data. Secondly, a schedulable capacity evaluation model based on model-agnostic meta-learning, convolutional neural network, long short-term neural network and attention mechanism (MAML-CNN-LSTM-Attention) is proposed. Through the model, the aggregated schedulable capacity of shared EVs in different functional communities for the coming 60 minutes is predicted. Model uses MAML to fine-tune the meta-prediction network through multi-task training to quickly adapt to feature changes caused by different travel habits of different functional communities; CNN-LSTM is used to learn spatial features of schedulable capacity and efficiently extract high-dimensional temporal features from historical sequences; Attention mechanism is used to further improve model prediction accuracy. Simulations show that the model proposed in this paper outperforms other existing models and can reliably predict the schedulable capacity for different date types and functional areas, providing useful decision aids for shared EV operators to participate in market services.

The aggregation of the remaining battery capacity of EVs can be used as distributed energy storage to participate in electricity market auxiliary services in the form of V2G, however, their reliability is affected by the uncertainty of EV travel. In this paper, we consider the uncertainty of EV load response and propose a resolution method for quantifying the reliability of EV aggregation capacity in the day-ahead market. The paper first classifies EV users by analyzing historical charging data. Secondly, based on the composite Poisson process, the V2G capacity and reliability model for the aggregator’s participation in the day-ahead auxiliary service bidding are proposed and validated using actual data. Based on this model, aggregators can decide the power of V2G bidding for any time slot on day t+1 according to the reliability requirements of the standby market for the standby capacity contracted before that day. Finally, this paper organically combines PV and EV aggregators in the microgrid and uses genetic algorithms to optimize the simulation of the microgrid voltage operation state under different scenarios in the IEEE 33-node system. The results show the effectiveness of the coordinated optimization scheme of PV and EV clusters for the optimization of microgrid voltage.

Large penetration of renewable energy sources into the power grid has increased the complexity of power system operation and greatly reduced the ability of the system to withstand large disturbances. The frequent occurrence of voltage instability problems in the power grid has brought new challenges to the assessment of transient stability analysis of the power system. To achieve fast and accurate assessment of transient voltages, a transfer learning-based transient voltage stability assessment with small samples is proposed, introducing a domain transfer learning approach, embedding a cooperative attention mechanism in the residual network during the feature extraction stage to capture long-range correlations between features, and using adversarial approaches to reduce the differences between samples from different data sets, using the source domain to guide the target domain for network training to improve model's evaluation capability when the number of samples is insufficient, enhance the generalisation performance of the network, and effectively improve the performance of real-time power system transient voltage stability evaluation in the absence of sufficient historical data. Testing on an improved New England 39-node system validates the superiority of this method in transient voltage stability assessment and provides a new approach to practical field transient voltage stability assessment.