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The promising prospect of a terahertz metasurface in sensing and detection applications has attracted increasing attention because of its ability to overcome the classical diffraction limit and the enhancement of field intensity. In this work, a novel scheme based on an all-silicon terahertz plasmon metasurface is proposed and experimentally demonstrated to be a highly sensitive biosensor for the Bacillus thuringiensis Cry1Ac toxin. The regression coefficients between Bacillus thuringiensis protein concentrations and the spectral resonance intensity and frequency were 0.8988 and 0.9238, respectively. The resonance amplitude variation and frequency shift of the metasurface were investigated in terms of both thickness and permittivity change of the analyte, which reflected the protein residue in the actual process. Moreover, the reliability and stability of the metasurface chip were verified by time period, temperature, and humidity control. These results promise the ability of the proposed metasurface chip as a Bacillus thuringiensis protein sensor with high sensitivity and stability. In addition, this novel device strategy provides opportunities for the advancement of terahertz functional applications in the fields of biochemical sensing and detection.

To address the problems of limited analysis scenarios in mathematical optimization methods and conservative assessment in random scenario simulation methods, this paper first introduces the concept of overvoltage risk and proposes an overvoltage risk-based PV capacity assessment method for distribution networks. Based on this method, the severity of overvoltage is further considered, and the overvoltage risk index is improved by introducing correction coefficients. The assessment results obtained from this method can better reflect the real overvoltage risk level of the distribution network, which is more adaptable and meaningful for actual PV capacity planning. Finally, a practical 55-bus distribution system in a region of China is used as an example to verify the adaptability and effectiveness of the proposed method.

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In the context of low carbon economy, introducing carbon trading and developing low-carbon energy generation is an important means to realize low-carbon development of the power system. Because gas power generation has the advantages of high efficiency, low carbon emission and strong peak load capability, the gas generator unit is added to the planning plan and a low carbon power planning model based on carbon trading is established. The goal of the model is to minimize the cost of the system integration. The cost includes investment operation cost and carbon transaction cost. And the natural gas supply constraints and carbon trading constraints are increased in the constraint condition. Finally, the discrete bacterial colony chemotaxis algorithm is adopted to solve this model. Through the model comparison and sensitivity analysis, it is concluded that the addition of gas turbine unit and carbon trading mechanism can optimize the power supply structure, promote the construction of low carbon unit. and realize the conclusion of low carbon emission reduction of power system. And the results verify the effectiveness of the proposed power planning model.