As a representative form of new energy generation, wind power has effectively alleviated environmental pollution and energy shortages. This paper constructs a green economic indicator to measure the degree of coordinated development of environmental and social benefits. To increase the amount of wind power consumption, an economic dispatch model based on the coordinated operation of cogeneration units and electric boilers was established; we also introduced the green certificate transaction cost, which effectively meets the strategic needs of China’s energy low-carbon transformation top-level system design. Wind power output has instability and volatility, so it puts higher requirements on the stable operation of thermal power units. To solve the stability problem, this paper introduces the output index of the thermal power unit and rationally plans the unit combination strategy, as well as introducing the concept of chance-constrained programming due to the uncertainty of load and wind power in the model. Uncertainty factors are transformed into load forecasting errors and wind power prediction errors for processing. Based on the normal distribution theory, the uncertainty model is transformed into a certain equivalence class model, and the improved disturbance mutated particle swarm optimization algorithm is used to solve the problem. Finally, the validity and feasibility of the proposed model are verified based on the IEEE30 node system.

Automated positioning of transmission tower keypoints is crucial for drone-based intelligent inspection systems. This paper proposes TPKE (Transmission Pylons Keypoint Extraction), a novel framework designed to extract multiple transmission tower keypoints from LiDAR point clouds. The method targets two core components: insulator string endpoints and ground wire hanging points. For insulator positioning, TPKE introduces adaptive density clustering, a morphological “concavity” index (η) for V-shaped insulators, and a “positioning-verification-compensation” strategy for handling missing data. For ground wire positioning, it combines local geometric feature analysis with spatial orthogonal projection. Using semantic segmentation for preprocessing, the framework reliably identifies components from complex transmission corridor point clouds. Validated on 1427 towers across 14 types, TPKE achieves an MAE of 0.0747 m for insulators and 0.0696 m for ground wires. It maintains centimeter-level accuracy even under challenging conditions like sparse point clouds. With an average processing time of 3.03 s per tower, the method demonstrates high efficiency, significantly reducing manual annotation workload while supporting autonomous navigation for transmission line maintenance.