Yiming Zhao

With the widespread deployment of smart meters in smart grids and the transformation of communication methods from wired to wireless, the communication security issues are becoming more and more prominent, especially facing dynamic attack threats such as malicious signal interference and meter data corruption, but there are also problems of low detection efficiency and resource waste. To address this problem, this paper proposes a dynamic attack and defense detection model based on Bayesian game under resource-constrained conditions. In order to characterize the game behavior of attackers and defenders under incomplete information conditions, a two-stage game process is designed to simulate attack scenarios such as eavesdropping and interference, and the Beta distribution is introduced for dynamic belief update to enhance the defender’s adaptability to attack situations. Combining the performance and communication characteristics of heterogeneous nodes, a bipartite graph matching mechanism is introduced, and the optimal allocation of defense resources is achieved based on the Hungarian algorithm, thereby reducing the overall defense cost while ensuring system security. The simulation experimental results show that the proposed model can effectively improve the defense success rate in a dynamic environment, which is better than random defense, full coverage defense and traditional machine learning methods. The strategy optimized by the Hungarian algorithm reduces costs and increases benefits. Its feasibility and efficiency in actual smart grid applications are verified.

In the context of growing global energy demand and increasingly severe environmental pollution, ensuring the stable dispatch of new energy sources and the effective management of power resources has become particularly important. This study focuses on the reliability and stability issues of new energy dispatch considering the complementary advantages of multiple energy types. It aims to enhance dispatch stability and energy utilization through an innovative Distributed Overlapping Coalition Formation (DOCF) model. A distributed algorithm utilizing tabu search is proposed to solve the complex optimization problem in power resource allocation. The overlapping coalitions consider synergies between different types of resources and intelligently allocate based on the heterogeneous demands of power loads and the supply capabilities of power stations. Simulation results demonstrate that DOCF can significantly improve power grid resource utilization efficiency and dispatch stability. Particularly in handling intermittent power resources such as solar and wind energy, the proposed model effectively reduces peak shaving time and improves the overall network energy efficiency. Compared with the preference relationship based on selfish and Pareto sequence, the PGG-TS algorithm based on BMBT has an average utility of 10.2% and 25.3% in terms of load, respectively. The methodology and findings of this study have important theoretical and practical value for guiding actual energy management practices and promoting the wider utilization of renewable energy.

There are many challenges in incorporating the attenuation cost of energy storage into the optimization of microgrid operations due to the randomness of renewable energy supply, the high cost of controlled power generation, and the complexity associated with calculating the cost of battery attenuation. Therefore, this paper proposes a microgrid energy management scheme considering the attenuation cost of energy storage. This scheme analyzes the power generation mode and uncertainty factors of distributed generators in detail. The influence of charge and discharge depth on the cycle life and residual value of the energy storage system was analyzed, and the energy storage attenuation cost model was established. Finally, considering the cost of power generation, environmental treatment, and the deterioration cost of energy storage systems, the objective function of the comprehensive operation cost of microgrids is formulated. The improved sine cosine algorithm (SCA) is used to simulate the energy output optimization of various distributed generators in the microgrid. The results show that the algorithm can effectively reduce the comprehensive operation cost of microgrids and improve their energy utilization efficiency, which proves the practical significance and reference value of the method for microgrid energy management.

This study introduces a novel stochastic framework for self-healing active distribution networks, addressing critical challenges posed by the integration of distribution networks (DNs), such as increased short-circuit capacity, voltage instability, and load variability. By employing graph theory for optimal network restructuring and incorporating stochastic programming to account for load uncertainties, the proposed method ensures robust and efficient fault recovery. The framework integrates innovative security enhancements, including adaptive short-circuit capacity minimization and dynamic loadability improvement. Using the pelican optimization algorithm (POA), the method achieves superior performance in real-world scenarios. The proposed framework’s unique integration of stochastic modeling, adaptive security mechanisms, and optimization techniques sets a new benchmark for self-healing smart grids. Simulations on IEEE 33-bus and 83-bus networks demonstrate the framework’s efficacy. The results highlight a 20% reduction in fault recovery time and a 12.5% decrease in power losses.