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Improving the resilience of the distribution grid to extreme rainstorms can effectively reduce the resulting outage losses. Based on the massive number of electric vehicles (EV) with objective capacity in the distribution network environment, it is essential to study the discharge of EVs to make up for the shortage load in the distribution network’s post-disaster recovery. In this paper, we first establish a coupling network integrating the distribution grid and road network, simultaneously coupling information from different levels. Then establish a model describing the characteristics of EV travel behavior based on the travel chain and probability distribution function to portray the spatial distribution of EVs in the time dimension. Then establish a model of EVs in this area and cross-area EV participating in discharge response based on the area after EV clustering. Also, establish the extreme storm weather affected EV discharge willingness model to describe the uncertainties of EVs. Finally, two faults set in the IEEE33 node are simulated and verified, and the model description and analysis of the influencing factors of EV participation in discharge are given.

Integrated demand response contributes to flexibility improvement in multi-energy system. Yet, the challenge posed by the load estimation mistake needs to be addressed to explore the available flexibility. In this paper, we present a price-based nonlinear integrated demand response for multi-energy microgrid against estimation mistakes of both electricity and gas loads. Among them, we develop a period partition method to describe load characteristics and reach a reasonable time-of-use pricing. Besides, a credibility theory-based risk measurement is presented to evaluate the uncertain wind power. Finally, case studies demonstrate the feasibility and effectiveness of the proposed method in flattening load curve, reducing operation cost and promoting the accommodation of wind power.

Nanofiltration application for the separation of Mg2+-Li+ from salt-lake brines was attempted in the present work. Four different nanofiltration membranes identified in the manuscript as DL, DK, NF-270, and NF-90 were used to treat salt brine with a magnesium to lithium ratio (MLR) of 61, additionally contaminated by the other ions such as Na+, K+, Ca2+, etc. The effect of the dilution factor, operating pressure, circulation rate, and feed pH were assessed to identify the optimal operating conditions for each membrane based on the retention efficiency of each ion. The results showed an insignificant effect of Ca2+ on the retention performance of Mg2+-Li+. Na+ and K+ had a smaller hydration radius and larger diffusion coefficient, which competed with Li+ and altered the separation of Mg2+-Li+. Under the optimal conditions (dilution factor: 40; operating pressure: 1.2 MPa; circulation flow rate: 500 L/h; pH: 7), the retention efficiency of lithium was as low as 5.17%, separation factor (SF) was as low as 0.074, and the MLR in the permeate reduced to 0.088.

With the increasing demand for lithium, the shortage of resources has become increasingly apparent. In order to conserve resources and to improve recovery, the extraction of lithium from salt lakes has become mandatory for sustainable development. Porous metal-organic framework (MOF) materials have attracted extensive attention due to their high/tunable porosity, pore function, multiple pore structures/compositions, and open metal sites. Moreover, MOFs combine the advantages of other porous materials and have a wide range of applications, which have received significant interest from the scientific community. Therefore, the selection of MOFs materials, the optimization of preparation methods, and the research of lithium separators are key directions to improve the total yield of lithium resources in salt lakes in China. This study aims to improve the comprehensive utilization of resources after lithium extraction and strengthen the engineering technology research of lithium extraction from salt lakes. This study can help to achieve the goal of efficient, integrated, and sustainable utilization of salt lake resources. An attempt has been made to summarize the types and preparation methods of MOFs materials, as well as the separation mechanism of MOFs nanofiltration membranes, with reference to its application in lithium extraction from salt lake brine. Finally, the future development of MOFs nanofiltration membranes for lithium extraction from salt lakes is also proposed.

Abstract Background Hyperlipidaemia may be a potential risk factor for the occurrence of intestinal polyps. This study aimed to evaluate correlation between lipidaemia and the formation of colorectal polyps. Methods One hundred and fourteen patients with colorectal polyps and forty-eight healthy controls were included in this study. Colonoscopies were performed for all patients and controls within 1 week before blood samples were taken. The concentrations of serum lipids and lipoproteins were measured simultaneously using an automatic biochemical analyser. The colorectal lesions were classified based on pathological characteristics, and four types were identified in the study: hyperplastic polyp (HP), tubular adenoma (TA), tubulovillous adenoma (TVA) and adenoma with high-grade dysplasia (A-HGD). Advanced adenoma was classified according to the number, size and histological type of polyps. Results The value of low-density lipoprotein cholesterol (LDL-C) was significantly higher in the group with advanced adenoma than in the controls (p

As objects of load prediction are becoming increasingly diversified and complicated, it is extremely important to improve the accuracy of load forecasting under complex systems. When using the group method of data handling (GMDH), it is easy for the load forecasting to suffer from overfitting and be unable to deal with multicollinearity under complex systems. To solve this problem, this paper proposes a GMDH algorithm based on elastic net regression, that is, group method of data handling based on elastic net (EN-GMDH), as a short-term load forecasting model. The algorithm uses an elastic net to compress and punish the coefficients of the Kolmogorov–Gabor (K–G) polynomial and select variables. Meanwhile, based on the difference degree of historical data, this paper carries out variable weight processing on the input data of load forecasting, so as to solve the impact brought by the abrupt change of load law. Ten characteristic variables, including meteorological factors, meteorological accumulation factors, and holiday factors, are taken as input variables. Then, EN-GMDH is used to establish the relationship between the characteristic variables and the load, and a short-term load forecasting model is established. The results demonstrate that, compared with other algorithms, the evaluation index of EN-GMDH is significantly better than that of the rest algorithm models in short-term load forecasting, and the accuracy of prediction is obviously improved.