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This paper proposes a novel inertia-enhanced distributed control method to complement the inertia of microgrids. The rate of change of frequency (RoCoF) and the rate of change of voltage (RoCoV) are employed in this paper to quantify the frequency inertia and voltage inertia, respectively. Then, a fully distributed algorithm with constrained changing rates is proposed. By bounding the changing rates of frequency and voltage during the consensus control, the algorithm can address the consensus problem while enhancing the inertia of microgrids. Compared with most inertia control methods, the proposed method can utilize the reserve power of scattered DGs to supply inertia. Besides, it performs better under disturbances and delays than conventional distributed control methods. The effectiveness of the proposed method is validated by several cases in MATLAB/Simulation and a hardware experiment. [ABSTRACT FROM AUTHOR]

This paper proposes a tube model predictive control method considering uncertainties of renewable energy generation in the low-voltage distribution network. For relatively low dependence on communication networks, a decentralized voltage control framework is urgently needed to be developed for voltage regulation in the low-voltage distribution network. Opposed to the traditional voltage control techniques, a novel decentralized tube model predictive voltage control (TMPC) strategy is proposed. First, this strategy entails the formulation of a discrete model encompassing distributed photovoltaic units and energy storage systems intertwined with the distribution grid model based on voltage sensitivity. Then, considering the worst case of voltage deviations caused by uncertain and stochastic power fluctuation, we specifically discuss a robust positively invariant to determine the control section of controllers. Further, the strategy employs tube model predictive controllers encompassing nominal and auxiliary controllers in distribution areas. The nominal controller generates a central path for different voltage operating points of each distribution area, and the auxiliary controller makes all trajectories lie in a bounded neighborhood called a tube. The effectiveness of the proposed control strategy is investigated on a test system containing renewable energy resources. • A novel decentralized control structure is proposed to serve voltage control for distribution networks, which can cooperate for accurate voltage control with random and fluctuant renewable energy. • In the nominal controller, operation points of DGs are generated considering the generation and load scheduling. In the auxiliary controller, detailed control objectives and constraints are proposed for suppressing disturbances of DGs. • To balance the conservation and performance, a worst-case-based robust positively invariant is developed to quantify the range of tubes to cope with the randomness. [ABSTRACT FROM AUTHOR]

Nucleotide oligomerization domain (NOD) like receptor X1 (NLRX1) is a member of pattern recognition receptor, which has been linked to viral response, cancer, and inflammatory diseases. In this study, rabbit NLRX1 (rNLRX1) was firstly cloned from RK-13 cells, which protein contained a NACHT domain and seven LRRs. rNLRX1 was widely expressed in tissues of rabbits, and highly increased in liver, spleen, kidney, and colon after infected with enterohemorrhagic Escherichia coli (EHEC). Overexpression of rNLRX1 negatively regulated NF-κB signaling, and impaired the expression of pro-inflammatory cytokines and defensins. Moreover, deficient of rNLRX1 in RK-13 cells was performed to investigate the possible roles of rNLRX1. Upon EHEC stimulation, knockdown of rNLRX1 markedly enhanced NF-κB activation and downstream responsive cytokines (IL1β and TNFα) and β-defensins (DEFB114 , DEFB124 , and DEFB125). Furthermore, overexpression of rNLRX1 promoted the proliferation of EHEC, whereas knockdown of rNLRX1 inhibited its growth. Our study identified that rNLRX1 acts as a negative regulatory in anti-microbial responses after EHEC infection. • rNLRX1 contained a NACHT domain and seven LRRs. • rNLRX1 highly increased after infected with enterohemorrhagic Escherichia coli. • rNLRX1 negatively regulated NF-κB signaling. • rNLRX1 impaired the expression of pro-inflammatory cytokine and defensins. • Knockdown of rNLRX1 markedly enhanced NF-κB activation. [ABSTRACT FROM AUTHOR]

• A novel model for low-voltage distribution areas based on genetic algorithm is developed, which simplifies the complexity of online computation and suppresses the inner voltage deviations in a safe section. • The coordination of PVs and ESs at different voltage levels is promoted to improve the voltage regulation while short of the reactive power capacity of PVs. • A tube-based MPC controller is proposed for voltage fluctuation under uncertainties introduced by power disturbance and a genetic-algorithm-based equivalent model of low-voltage areas, with which a distribution network can work in common conditions under variable operations of PVs. With the ever-growing penetration of distributed generators (DGs), voltage deviation caused by randomness and fluctuation has become increasingly prominent in distribution networks. Conventional voltage control cannot sufficiently utilize the rapid and flexible characteristics of DGs. In this paper, a tube-based model predictive control (TMPC) method is proposed to address voltage violation and fluctuation difficulties considering the uncertainties of renewable energy. Firstly, a simplified model approach based on a genetic algorithm (GA) is proposed to unify numerous DGs in low-voltage distribution areas. Then, based on this simplified model combined with the voltage sensitivity model, the TMPC controller is designed for the whole distribution network, which consists of two subsidiary controllers to suppress voltage deviation caused by uncertainties. The former is used to generate a central path for different operating points of the grid, and the latter is designed to track the path in a bound tube. The proposed method can cope with the uncertainties caused by external disturbances such as drastic power fluctuations. Finally, the numerical simulation in the case studies demonstrates the effectiveness of the proposed method in accuracy and efficiency. [ABSTRACT FROM AUTHOR]