Research on optimal sensor placement (OSP) has become very important due to the need to obtain effective testing results with limited testing resources in health monitoring. In this study, a new methodology is proposed to select the best sensor locations for large structures. First, a novel fitness function derived from the nearest neighbour index is proposed to overcome the drawbacks of the effective independence method for OSP for large structures. This method maximizes the contribution of each sensor to modal observability and simultaneously avoids the redundancy of information between the selected degrees of freedom. A hybrid algorithm combining the improved discrete particle swarm optimization (DPSO) with the clonal selection algorithm is then implemented to optimize the proposed fitness function effectively. Finally, the proposed method is applied to an arch dam for performance verification. The results show that the proposed hybrid swarm intelligence algorithm outperforms a genetic algorithm with decimal two-dimension array encoding and DPSO in the capability of global optimization. The new fitness function is advantageous in terms of sensor distribution and ensuring a well-conditioned information matrix and orthogonality of modes, indicating that this method may be used to provide guidance for OSP in various large structures.

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The volatility of a new energy output leads to bidding bias when participating in the power market competition. A pumped storage power station is an ideal method of stabilizing new energy volatility. Therefore, wind power suppliers and pumped storage power stations first form wind storage joint ventures to participate in power market competition. At the same time, middlemen are introduced, constructing an upper-level game model (considering power producers and wind storage joint ventures) that forms equilibrium results of bidding competition in the wholesale and power distribution markets. Based on the equilibrium result of the upper-level model, a lower model is constructed to distribute the profits from wind storage joint ventures. The profits of each wind storage joint venture, wind power supplier, and pumped storage power station are obtained by the Nash negotiation and the Shapely value method. Finally, a case study is conducted. The results show that the wind storage joint ventures can improve the economics of the system. Further, the middlemen can smooth the rapid fluctuation of power price in the distribution and wholesale market, maintaining a smooth and efficient operation of the electricity market. These findings provide information for the design of an electricity market competition mechanism and the promotion of new energy power generation.

China's power industry is facing the issue of reducing carbon emissions, a particularly important matter to address during the industrial development. Based on the emission reduction status of power industries in China, the possibilities and the challenges of dealing with climate change for Chinese power industries are discussed in this paper by employing PEST (political, economic, social, technological)-strengths, weaknesses, opportunities, and threats analysis. The Tradable Green Certificate (TGC) system and the Carbon Emission Trading scheme for power industry development and environmental protection are analyzed as well. The results show that (1) the possibilities of developing power industries and addressing the climate change issue involve internal advantages (three strengths) and external chances (four opportunities); (2) the challenges for the Chinese power industry involve internal disadvantages (three weaknesses) and external unfavorable factors (four threats); and (3) both the TGC planning and the carbon emission scheme, as an efficient market-oriented strategic change, can jointly adjust the structure of power industries.

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