退役新能源组件绝氧热解处置技术

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三项硬核技术亮相 能源科技再添新引擎
Zhong Guo Dian Li Bao· 2025-09-18 08:30
Core Insights - The recent "New Tian Gong Kai Wu - Technology Achievements Release Conference" showcased innovative energy technologies aimed at addressing energy shortages and enhancing the recycling of retired renewable energy components [1] Group 1: Marine Energy Technology - The ocean covers approximately 71% of the Earth's surface and contains abundant renewable blue energy resources, including wave and tidal energy, which are considered ideal low-carbon renewable energy sources [2] - The average wave energy density globally is between 30 to 70 kW per meter, while China's coastal wave energy density is significantly lower, ranging from 5.1 to 7.7 kW per meter, presenting a challenge for utilization [2] - The invention of the Triboelectric Nanogenerator (TENG) technology allows for efficient capture of low-frequency, small-amplitude wave energy, with peak power density output increased from a few watts per cubic meter to 114.8 watts per cubic meter [2] Group 2: Recycling of Retired Components - China's renewable energy industry is experiencing rapid growth, with the installed capacity of wind, solar, and energy storage reaching new heights, leading to an expected retirement volume of over 5 million tons of components by 2030 [3][4] - The developed oxygen-free pyrolysis technology enables the high-value recycling of retired components, converting wind turbine blades into glass fibers and pyrolysis oil, with fiber strength reaching over 90% of the original material and pyrolysis oil having a calorific value of 25 MJ per kilogram [4] - The technology has been successfully implemented across more than ten provinces in China, achieving an annual processing target of tens of thousands of retired components, demonstrating its engineering stability and economic viability [4] Group 3: Power Prediction Systems - The increasing share of renewable energy in the energy structure presents challenges due to the randomness, volatility, and intermittency of renewable power generation [5] - A new power prediction system developed by Shenzhen Energy Group enhances the accuracy of meteorological predictions at the station level by 15% and wind power predictions by 10%, improving grid dispatch decision-making [5][6] - The system employs a three-level downscaling architecture and integrates artificial intelligence, resulting in a 10% reduction in prediction error compared to traditional interpolation methods, with significant economic benefits reported [6][7]