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 paper presents a method to generalize locational marginal prices (LMPs) to embed the impact of system frequency dynamics into real-time electricity markets. The proposed frequency dynamics-aware LMPs can help to mitigate costs associated with setting aside ever more reserve capacity to offset larger, faster, and more frequent transient excursions arising from greater renewable integration. We formulate a dynamics-aware economic dispatch (ED) by augmenting a traditional static ED with constraints pertinent to system frequency dynamics, including those from inertial response, primary frequency control, and the automatic generation control. We show that, similar to their traditional static counterparts, dynamics-aware LMPs are composed of Lagrange multipliers associated with the power balance and transmission line power flow constraints. Furthermore, through analysis, we detail dynamic and steady-state behaviours of dynamics-aware LMPs. Finally, numerical simulations involving standard test systems validate our findings, confirm added revenue opportunities for generators contributing to frequency support, and demonstrate computational scalability.

To improve the accuracy of wildfire risk identification in areas adjacent to power transmission corridors, this study proposes a wildfire early warning method that integrates refined land cover segmentation and multimodal feature deep learning. First, an improved bi-branch semantic segmentation network (BuildFormer++) is used to perform refined classification of high-resolution remote sensing images, extracting six types of land cover information, including forest and cultivated land. Second, a multi-dimensional feature set integrating land cover, topography, climate, and human activities is constructed and input into a multimodal wildfire point prediction network for deep feature fusion and probabilistic modeling. Experimental results show that the proposed segmentation network achieves a mean intersection–union ratio (mIoU) of 40.68% in the semantic segmentation task; the early warning model achieves an accuracy of 85.37%, an F1 score of 93.15%, and an ROC-AUC of 85.42% in risk prediction, significantly outperforming comparative methods. The “refined segmentation–feature fusion–risk prediction” framework constructed by this method can provide reliable technical support for the operation and maintenance safety and fire prevention of power transmission corridors.

Traditional single tuned filter can only filter a special frequency harmonic for power system. A novel three-tuned filter equaling to three single tuned filters is proposed in the paper. The filter is constituted with three capacitors and three reactors. The algorithm for determining the filter's parameters is also presented. A three-tuned filter for a 110 kV power grid is designed and simulated. The results show that the filter can filter three different given frequency harmonics, and it has better performance than three single-tuned filters, such as only one reactor subjected to full line voltage, less space occupation, only one switchgear needed, and low hardware cost etc

The increasing demand for low-latency, computationally intensive vehicular applications, such as autonomous navigation and real-time perception, has led to the adoption of cloud–edge–vehicle infrastructures. These applications are often modeled as Directed Acyclic Graphs (DAGs) with interdependent subtasks, where precedence constraints enforce causal ordering while allowing concurrency. We propose a task offloading framework that decomposes applications into precedence-constrained subtasks and formulates the joint scheduling and offloading problem as a Markov Decision Process (MDP) to capture the latency–energy trade-off. The system state incorporates vehicle positions, wireless link quality, server load, and task-buffer status. To address the high dimensionality and sequential nature of scheduling, we introduce DepSchedPPO, a dependency-aware sequence-to-sequence policy that processes subtasks in topological order and generates placement decisions using action masking to ensure partial-order feasibility. This policy is trained using Proximal Policy Optimization (PPO) with clipped surrogates, ensuring stable and sample-efficient learning under dynamic task dependencies. Extensive simulations show that our approach consistently reduces task latency, energy consumption and QOS compared to conventional heuristic and DRL-based methods. The proposed solution demonstrates strong applicability to real-time vehicular scenarios such as autonomous navigation, cooperative sensing, and edge-based perception.