Smart Microgrid with distributed energy resources is prevalent due to its flexibility and adaptability. Benefiting from Internet of Things technology, massive real-time energy data can be gathered for intelligent energy management. However, the high stochasticity of renewable energy resources makes it difficult to design an optimal scheduling strategy for smart microgrid. To address this problem, a dynamic energy management strategy based on model-free reinforcement leaning is proposed. Considering the uncertainties of renewable resources and energy demands, the dynamic energy management problem is formulated as a Markov decision process with unknown transition probabilities. Then, an intelligent energy management strategy based on soft actor-critic is proposed. Moreover, gate recurrent unit is incorporated to enhance the extraction of time-series characteristics. Finally, simulation results demonstrate that the proposed method significantly reduces the energy bills by up to 24.28%, compared with other strategies.

Microgrids have emerged as a promising solution to improve the resilience and reliability of power systems. However, unexpected faults can still pose significant challenges to the stable operation of microgrids. This paper proposes a stochastic programming-based strategy for mitigating faults in a DC microgrid. First, a scheduling strategy is implemented for normal operations and switched to a mitigation strategy upon detecting faults, which ensures optimal performance and resilience. Second, a scenario-based stochastic programming enhanced by the DBSCAN algorithm for scenario reduction is designed, which addresses the optimization problem efficiently. Comprehensive simulations are conducted to evaluate the proposed scheduling and mitigation strategies, demonstrating their advantages under both normal and fault conditions.

Grid operation state estimation of power grid operating states and power system analysis largely relies on physical layer measurements of the power system. However, the introduction of information and communication technologies in smart grids has greatly improved operational efficiency while also increasing the system’s vulnerability to attack. This can compromise data integrity and reliability, leading to data missing and then affecting subsequent steps. Additionally, data collection and transmission can encounter various issues, resulting in partial data loss or errors. Therefore, it is crucial to recover and complete the missing data.This paper proposes a solution for missing data recovery in power systems based on generative adversarial network (GAN). The approach utilizes graph convolutional network (GCN) to extract features from data, taking into account the topological connectivity between nodes. To improve the quality of data imputation and enhance the authenticity of recovered data, incomplete data containing nodes with missing data and observable nodes is used as input for the generator, and a local feature extractor is added to the existing discriminator network. This structure allows the generator to estimate unknown information by observing known data, while the discriminator focuses more on the local areas containing the recovered data when determining the authenticity, thereby helping the generator to improve its data completion capability. Correspondingly, we design context loss constraints considering both local and global ranges to ensure accurate recovery of non-missing node data while completing the missing data portions.The experimental results demonstrate that employing GCN and incorporating local features can significantly enhance recovery performance on grid data. For voltage magnitude, the mean absolute error decreased by 10.4335%. Additionally, high-precision recovery results can still be achieved even with up to half data missing, which is validated on the IEEE-14 system.

The energy scheduling strategy for microgrids based on reinforcement learning plays a very crucial role in realizing low-carbon and economic energy utilization. Considering the traditional reinforcement learning is difficult to meet the operational constraints of microgrids, this paper proposes a safety reinforcement learning method based on proximal policy optimization. Firstly, ladder-type carbon trading is introduced to strictly constrain the system carbon emission. Then, a safety proximal policy optimization method is proposed that incorporates a safety network to decouple the economic and safety factors in the traditional reward composition. Finally, experiments are conducted on real-world datasets to verify the effectiveness of the proposed algorithm. The experimental results illustrate that the proposed safety reinforcement learning method is able to minimize the economic cost and carbon emission of microgrid scheduling while strict guaranteeing safety compared to existing methods.

The integration of advanced communication and information technologies in smart grids has led to enhanced efficiency and reliability but also introduced security vulnera-bilities, prompting the need for robust cyber attack detection methods. Traditional approaches struggle to capture evolving attack patterns and handle high-dimensional data, highlighting the necessity for more sophisticated approaches. A neighbor-hood reconstruction-based smart grid attack detection scheme based on subgraphs is proposed. By leveraging Graph Neural Networks (GNNs), the challenge of capturing complex inter-dependencies among grid nodes is addressed. This approach employs unsupervised learning principles, training the model solely on normal data and utilizing the reconstruction error of node features to detect attacks. Additionally, by subgraph sampling and feature suppression, the model's ability to utilize neighborhood information is enhanced, thereby further improving detection effectiveness. Simulation results on IEEE 30-bus and IEEE 118-bus power system demonstrate the feasibility of the method, achieving a detection accuracy of 96.67% and 97.46%, respectively.

The braking system is the key part of trains, and its full life-cycle of health status is essential to ensure the safety of trains. How to accurately assess real-time health status throughout the full life-cycle of the train braking system is a challenge. In this paper, a digital twin-driven hybrid estimate method for health status of the braking system is proposed. Firstly, an equivalent model of the braking system is built in the digital twin platform. Then, a hybrid method of fusing model and data is proposed to assess the health status. Finally, a cloud digital twin experimental platform for health status assessment of the braking system is built, and the health status is shown by visualization framework. The experiments verify the effectiveness and practicality of the proposed scheme.