To address the frequent external damage incidents to transmission line corridors caused by construction machinery such as excavators and cranes, this paper constructs a dataset of external damage hazards in transmission line corridors and proposes a detection method based on YOLO-LSDW for these hazards. Firstly, by incorporating the concept of large separable kernel attention (LSKA), the spatial pyramid pooling layer is improved to enhance the information exchange between different feature levels, effectively reducing background interference on external damage hazard targets. Secondly, in the neck network, the traditional convolution is replaced with a ghost-shuffle convolution (GSConv) method, introducing a lightweight slim-neck feature fusion structure. This improves the extraction capability for small object features by fusing deep semantic information with shallow detail features, while also reducing the model’s computational load and parameter count. Then, the original YOLOv8 head is replaced with a dynamic head, which combines scale, spatial, and task attention mechanisms to enhance the model’s detection performance. Finally, the wise intersection over union (WIoU) loss function is adopted to optimize the model’s convergence speed and detection performance. Evaluated on the self-constructed dataset of external damage hazards in transmission line corridors, the improved algorithm shows significant improvements in key metrics, with mAP@0.5 and mAP@0.5:0.95 increasing by 3.4% and 4.6%, respectively, compared to YOLOv8s. Additionally, the model’s computational load and parameter count are reduced, and it maintains a high detection speed of 96.2 frames per second, meeting real-time detection requirements.

In recent years, the global power industry has experienced rapid development, with significant advancements in the source, network, load sectors, and energy storage technologies. The secure, reliable, and economical operation of power systems is a critical challenge. Due to the stochastic nature of intermittent renewable energy generation and the coupled time-series characteristics of energy storage systems, it is essential to simulate uncertain variables accurately and develop optimization algorithms that can effectively tackle multi-objective problems in economic dispatch models for microgrids. This paper proposes a pelican algorithm enhanced by multi-strategy improvements for optimal generation scheduling. We establish eight scenarios with and without pumped storage across four typical seasons—spring, summer, autumn, and winter—and conduct simulation analyses on a real-world case. The objective is to minimize the total system cost. The improved pelican optimization algorithm (IPOA) is compared with other leading algorithms, demonstrating the validity of our model and the superiority of IPOA in reducing costs and managing complex constraints in optimization.

With the advancement and development of the electricity market (EM), carbon emission market (CEM), and renewable energy certificate market (RECM), promoting the integration and growth of the EM alongside carbon emission trading, renewable energy certificate trading, and other related markets is becoming increasingly important for high-quality development of the power industry. Analyzing the intrinsic connections among these three types of markets can facilitate their coordinated development. In this study, we selected monthly data on European Union (EU) carbon emission futures, French electricity trading prices, and the price of Guarantees of Origin (GO) in France from March 2019 to March 2024 and utilized the Bayesian time-varying stochastic volatility vector autoregression model (TVP-SV-VAR) with time-varying parameters to effectively capture the dynamic changes among the three markets and to analyze the relationships and characteristics of the EM, CEM, and RECM across different historical contexts. Simulation results showed that the influences of the EM and CEM on the RECM were relatively low, with more pronounced short-term effects and relatively stable medium- and long-term effects. In contrast, the influences of the CEM and RECM on the EM were significant, with more pronounced short-term effects and stable medium- and long-term effects. The influences of the EM and RECM on the CEM were significant in the short term.