Shidong Hong

The underdeveloped power and communication infrastructures in remote areas are prone to simultaneous failure during extreme disasters, exacerbating the difficulty of power system recovery. Mobile energy storage vehicles (MESVs) and unmanned aerial vehicles (UAVs) can achieve the coordination of power and communication, providing precise and effective power support for remote power grid. However, the coordinated planning of MESVs and UAVs is constrainted by factors such as endurance, coverage, and connection. The relationship between these constraints and the performance of dispatching flexibility and resilience support has not been well investigated. This paper proposes a deployment-dispatching two-stage coordinated planning framework of UAVs and MESVs to enhance the remote power grid resilience. The topology-based UAV deployment model and the power flow based MESV deployment mode is proposed. The coordinated dispatching model for UAVs and MESVs considering communication availability is proposed. The test case on IEEE 33 node system and application case on a remote power grid in Northwest China is conducted. The results show that the spatial dispatching flexibility in exchange for communication and traffic constraints significantly improves the resilience support performance of MESVs.

As the proportion of renewable power continues to increase, its inherent intermittency and volatility pose serious challenges to the security and stability of power systems. Scenario generation technology serves as a key tool supporting decision-making methods such as stochastic optimization and risk analysis. By generating representative power output scenarios, it can effectively characterize the uncertainty of renewable power output. This paper systematically reviews mainstream methods for the scenario generation of renewable power output, categorizing them into two major classes: sampling-based methods and model-based methods. Among them, sampling-based methods include Monte Carlo sampling, Latin hypercube sampling (LHS), Markov chains (MCs), and Copula functions. Model-based methods encompass artificial neural networks (ANNs), long short-term memory networks (LSTMs), autoregressive moving average models (ARMAs), generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models and transformer-based models. This paper elaborates on the principles and characteristics of each type of method. Moreover, scenario quality is evaluated from three dimensions: output-based metrics for numerical accuracy, distribution-based metrics for statistical consistency, and event-based metrics for key operational event representation. The current research challenges and future research directions are also summarized to provide a reference for modeling the uncertainty of renewable output.

The high penetration of renewable energy is becoming an important feature of new power systems. However, the power grid is facing greater threats of failures with the increasing frequency of extreme weather, making it necessary to enhance the resilience of power systems. In this paper, a multi-time-scale energy storage planning system is proposed for power system resilience improvement. Firstly, the characteristics of multi-time-scale energy storage are analyzed, and models of battery energy storage and hydrogen energy storage are established. Secondly, based on an analysis of random extreme weather scenarios, a bi-level stochastic programming model for multi-energy storage aimed at enhancing the resilience of power systems is constructed. Finally, based on the modified IEEE-24 node system, the model solution and example analysis are carried out, and the optimal configuration scheme for multi-energy storage is obtained. The results show that multi-energy storage is able to adjust more flexibly and effectively improve the resilience of the power system. Compared with the configurations of short-term and long-term energy storage systems, adopting multi-timescale energy storage reduces the total cost by 22.77% and 14.08%, respectively, and improves resilience by 4.33% and 0.67%, respectively.