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The optimization of the symmetry of MTDC systems after a contingency is crucial for the stable and economic operation of the MTDC systems. In this paper, a multi-objective optimal control method for the power flow symmetry of MTDC systems for the large-scale integration of offshore wind farms is proposed. A mirror relationship between the available headroom of DC lines and VSCs and their actual power flow distribution performance is established. A corresponding symmetry index is established for the MTDC network, and the multi-objective optimization problem is converted into a series of single-objective problems by the normal boundary intersection method, and solved by the original dyadic interior point method, so as to obtain the Pareto optimal solution with uniform distribution. The compromise optimal solution is decided according to the entropy weight double-basis point method, which provides decision-making guidance for the operators. The simulation results show that the normal boundary intersection method can solve the multi-objective dynamic optimal control problem of the VSC-HVDC system quickly and efficiently, and improve the symmetry of the power flow in an MTDC network. [ABSTRACT FROM AUTHOR]

As the share of intermittent and fluctuating renewable energy sources in the power system continues to increase, demand‐side resources must be utilized to enhance the real‐time balance of power generation and supply. Data centres, as typical energy‐intensive loads with bidirectional supply and demand characteristics, are crucial demand‐side resources. However, their operation is complex, making reliable regulation difficult. This paper establishes a refined process‐level load model for data centres by considering the temporal and spatial shifting characteristics of computational loads, effectively reflecting the intricate interactions between these loads and servers. Based on this model, a micro‐energy network is constructed that integrates renewable energy access and waste heat recovery from the data centre. This network also participates in carbon emission trading and green electricity certificate trading markets, applying an interaction mechanism. By considering data interaction and power sharing among multiple micro‐energy networks, the alternating direction method of multipliers algorithm is used to solve the system‐distributed optimization problem in two stages. This approach results in an optimal energy management scheme that simultaneously reduces total operation costs and carbon emissions. [ABSTRACT FROM AUTHOR]

This study investigated the enhancement of the mechanical and acoustic properties of polyurea (PU) composites by incorporating iron microparticles (Fe). The Fe/PU composites were prepared using a mixing process, where iron microparticles were evenly distributed within the polyurea matrix via mechanical stirring. The primary purpose of this study was to explore how varying volumes of iron microparticles influence the viscoelastic properties and acoustic performance of the composites, with a particular focus on applications in underwater acoustic stealth technology. Dynamic mechanical analysis was conducted to study the effect of the iron powder volume fraction on the mechanical properties of the composites, i.e., on the temperature-dependent storage moduli (E′), loss moduli (E″) and loss factor (tanδ). The intrinsic properties of the components and the nature of the interface between the phases determined the dynamic mechanical properties of the composite. It was found that the storage and loss moduli of the composite both increased as the iron volume fraction increased, which indicates that the incorporation of iron powder in a polyurea matrix appreciably induces reinforcing effects. Moreover, the tanδ curve of the Fe/PU composite generally had broader widths of the tanδ peak than those of pure polyurea, which showed better damping behavior. The height of the damping peaks depended on the iron powder content, and the glass transition temperature (measured as the temperature at the maximum value of the tanδ) shifted toward higher temperatures as the iron volume fraction increased. Also, the compression stress–strain curves of the composites obtained by a universal testing machine indicated that the addition of iron powder improved the compressive strength and the energy storage capacity of the composite. In addition, the acoustic absorption coefficient was found to increase with the addition of iron powder, and the increase in sample thickness was found to be the effective in improving low-frequency sound absorption. [ABSTRACT FROM AUTHOR]

Interaction behaviors are a crucial, distinctive feature of organisms in ecological systems. Inspired by nature, micro‐/nanorobots also show interaction behaviors with a significant number of novel and advanced functionalities. The micro‐/nanoscale size and self‐driven abilities of micro‐/nanorobots have attracted considerable attention for potential applications. The mission of micro‐/nanorobots has evolved from basic mechanical transfer or assistance to a diverse range of complex tasks, such as targeted drug delivery, biosensing, and environmental remediation. Herein, we focus on the interaction behavior of coexisting cooperative cognitive (Tri‐Co) micro‐/nanorobots, summarize recent research, and classify the interactions into three broad types: with the environment, with robot peers, and with human external control. [ABSTRACT FROM AUTHOR]

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