Exploring the low redshift universe: two parametric models for effective pressure

Astrophysical observations have put unprecedentedly tight constraints on cosmological theories. The $\Lambda$CDM model, mathematically simple and fits observational data-sets well, is preferred for explaining the behavior of universe. But many basic features of the dark sectors are still unknown, which leaves rooms for various nonstandard cosmological hypotheses. As the pressure of cosmological constant dark energy is unvarying, ignoring contributions from radiation and curvature terms at low redshift, the effective pressure keeps constant. In this paper, we propose two parametric models for non-constant effective pressure in order to study the tiny deviation from $\Lambda$CDM at low redshift. We recover our phenomenological models in the scenarios of quintessence and phantom fields, and explore the behavior of scalar field and potential. We constrain our model parameters with SNe Ia and BAO observations, and detect subtle hints of $\omega_{de}<-1$ from the data fitting results of both models, which indicates possibly a phantom dark energy scenario at present.Comment: 11 pages, 24 figure

Impact Analysis of Seismic Events On Integrated Electricity and Natural Gas Systems

Seismic events can cause devastating impacts on both overground and underground energy system infrastructure. This paper proposes a methodology to evaluate the impact of seismic events on the security of integrated electricity and gas system, mainly focusing on pipelines leakage and connection loss of electricity transmission lines. A stochastic model is used to formulate the damage level based on earthquake severity. The seismic impact on the integrated system is classified according to the levels of pipe leak and electricity line failure. Load curtailment due to limited generation capacity and overloaded transmission lines is thereafter quantified. Seismic intensity is generated randomly based on Monte Carlo simulation so that a certain seismic intensity can be related to relevant load curtailment. An integrated energy system with a 30-busbar electricity system and a 6-node natural gas network is used to demonstrate the effectiveness of the proposed method. The results clearly illustrate damage consequences under seismic events in terms of both probability and severity levels. This work can inform resilience enhancement scheme design based on the vulnerability performance and impact of both systems

Reliability-based Probabilistic Network Pricing with Demand Uncertainty

The future energy system embraces growing flexible demand and generation, which bring large-scale uncertainties and challenges to current deterministic network pricing methods. This paper proposes a novel reliability-based probabilistic network pricing method considering demand uncertainty. Network reliability performance, including probabilistic contingency power flow (PCPF) and tolerance loss of load (TLoL), are used to assess the impact of demand uncertainty on actual network investment cost, where PCPF is formulated by the combined cumulant and series expansion. The tail value at risk (TVaR) is used to generate analytical solutions to determine network reinforcement horizons. Then, final network charges are calculated based on the core of the Long-run incremental cost (LRIC) algorithm. A 15-bus system is employed to demonstrate the proposed method. Results indicate that the pricing signal is sensitive to both demand uncertainty and network reliability, incentivising demand to reduce uncertainties. This is the first-ever network pricing method that determines network investment costs considering both supply reliability and demand uncertainties. It can guide better sitting and sizing of future flexible demand in distribution systems to minimise investment costs and reduce network charges, thus enabling a more efficient system planning and cheaper integration.</p

A Two-Stage Resilience Enhancement for Distribution Systems Under Hurricane Attacks

Hurricane events can cause severe consequences to the secure supply of electricity systems. This article designs a novel two-stage approach to minimize hurricane impact on distribution networks by automatic system operation. A dynamic hurricane model is developed, which has a variational wind intensity and moving path. The article then presents a two-stage resilience enhancement scheme that considers predisaster strengthening and postcatastrophe system reconfiguration. The pre-disaster stage evaluates load importance by an improved PageRank algorithm to help deploy the strengthening scheme precisely. Then, a combined soft open point and networked microgrid strategy is applied to enhance system resilience. Load curtailment is quantified considering both power unbalancing and the impact of line overloading. To promote computational efficiency, particle swarm optimization is applied to solve the designed model. A 33-bus electricity system is employed to demonstrate the effectiveness of the proposed method. The results clearly illustrate that the impact of hurricanes on load curtailment, which can be significantly reduced by appropriate network reconfiguration strategies. This model provides system operators a powerful tool to enhance the resilience of distribution systems against extreme hurricane events, reducing load curtailment

Network pricing for smart grids considering customers' diversified contribution to system

This paper proposes a modified long-run incremental cost pricing (LRIC) method for distribution network pricing considering the diversified contributions of network users to system peak. The Shapley-value method and modified coincident factor method are used to determine network users' various contributions to the system. The comparison between original LRIC and the modified LRIC indicates the positive correlation between the contribution to system peak and network charges for different network users. This paper also explores the potential users' behavior change to gain bill reductions according to the cooperative-game theory and the consequential network investment deferral.</p

Enhanced oxidation resistance of active nanostructures via dynamic size effect.

A major challenge limiting the practical applications of nanomaterials is that the activities of nanostructures (NSs) increase with reduced size, often sacrificing their stability in the chemical environment. Under oxidative conditions, NSs with smaller sizes and higher defect densities are commonly expected to oxidize more easily, since high-concentration defects can facilitate oxidation by enhancing the reactivity with O2 and providing a fast channel for oxygen incorporation. Here, using FeO NSs as an example, we show to the contrary, that reducing the size of active NSs can drastically increase their oxidation resistance. A maximum oxidation resistance is found for FeO NSs with dimensions below 3.2 nm. Rather than being determined by the structure or electronic properties of active sites, the enhanced oxidation resistance originates from the size-dependent structural dynamics of FeO NSs in O2. We find this dynamic size effect to govern the chemical properties of active NSs

Proteomic analysis of young leaves at three developmental stages in an albino tea cultivar

Abstract Background White leaf No.1 is a typical albino tea cultivar grown in China and it has received increased attention in recent years due to the fact that white leaves containing a high level of amino acids, which are very important components affecting the quality of tea drink. According to the color of its leaves, the development of this tea cultivar is divided into three stages: the pre-albinistic stage, the albinistic stage and the regreening stage. To understand the intricate mechanism of periodic albinism, a comparative proteomic approach based on two-dimensional electrophoresis (2-DE) and mass spectrometry was adopted first time to identify proteins that changed in abundance during the three developmental periods. Results The 2-DE results showed that the expression level of 61 protein spots varied markedly during the three developmental stages. To analyze the functions of the significantly differentially expressed protein spots, 30 spots were excised from gels and analyzed by matrix-assisted laser desorption ionization-time of flight-tandem mass spectrometry. Of these, 26 spots were successfully identified. All identified protein spots were involved in metabolism of carbon, nitrogen and sulfur, photosynthesis, protein processing, stress defense and RNA processing, indicating these physiological processes may play crucial roles in the periodic albinism. Quantitative real-time RT-PCR analysis was used to assess the transcriptional level of differentially expressed proteins. In addition, the ultrastructural studies revealed that the etioplast-chloroplast transition in the leaf cell of White leaf No. 1 was inhibited and the grana in the chloroplast was destroyed at the albinistic stage. Conclusions In this work, the proteomic analysis revealed that some proteins may have important roles in the molecular events involved in periodic albinism of White leaf No. 1 and identificated many attractive candidates for further investigation. In addition, the ultrastructural studies revealed that the change in leaf color of White leaf No. 1 might be a consequence of suppression of the etioplast-chloroplast transition and damage to grana in the chloroplast induced by temperature. These results provide much useful information to improve our understanding of the mechanism of albinism in the albino tea cultivar.</p