Multi-Interval Rolling-Window Joint Dispatch and Pricing of Energy and Reserve under Uncertainty

In this paper, the intra-day multi-interval rolling-window joint dispatch and pricing of energy and reserve is studied under increasing volatile and uncertain renewable generations. A look-ahead energy-reserve co-optimization model is proposed for the rolling-window dispatch, where possible contingencies and load/renewable forecast errors over the look-ahead window are modeled as several scenario trajectories, while generation, especially its ramp, is jointly scheduled with reserve to minimize the expected system cost considering these scenarios. Based on the proposed model, marginal prices of energy and reserve are derived, which incorporate shadow prices of generators' individual ramping capability limits to eliminate their possible ramping-induced opportunity costs or arbitrages. We prove that under mild conditions, the proposed market design provides dispatch-following incentives to generators without the need for out-of-the-market uplifts, and truthful-bidding incentives of price-taking generators can be guaranteed as well. Some discussions are also made on how to fit the proposed framework into current market practice. These findings are validated in numerical simulations

An improved system for competent cell preparation and high efficiency plasmid transformation using different Escherichia coli strains

This paper describes an efficient bacterial transformation system that was established for the preparation of competent cells, plasmid preparation, and for the storage in bacterial stocks in our laboratory. Using this method, a number of different plasmids have been amplified for further experiments. Competent cells for bacterial transformation were prepared by the calcium chloride method with an optimum concentration of 75 mM. Three different strains of Escherichia coli that were tested are DH5\u3b1, TG1 and XL1 blue, and the most efficient strain being XL1 blue. The optimal optical density (OD600) range for competent cell preparation varied for each of the strains investigated, and for XL1 blue it was 0.15-0.45; for TG1 it was 0.2-0.5; and for DH5\u3b1 it was 0.145-0.45. The storage time of competent cells and its correlation to transformation efficiency has been studied, and the result showed that competent cells can be stored at -20\ub0C for 7 days and at -70\ub0C for 15 days. Three critical alterations to previous methods have been made, which are the changing of the normal CaCl2 solution to TB solution, the changing of the medium from LB to S.O.C., and addition of DMSO or PEG8000 during transformation of competent cells with plasmids. Changing the medium from LB to S.O.C., resulted in much faster growth of transformants, and the transformation efficiency was increased. Addition of DMSO or PEG8000 raised transformation efficiencies by 100-300 fold. Our improved bacterial transformation system can raise the transformation efficiency about 103 times, making it becoming a highly efficient bacterial transformation system

Organic crystal structure prediction via coupled generative adversarial networks and graph convolutional networks

Organic crystal structures exert a profound impact on the physicochemical properties and biological effects of organic compounds. Quantum mechanics (QM)–based crystal structure predictions (CSPs) have somewhat alleviated the dilemma that experimental crystal structure investigations struggle to conduct complete polymorphism studies, but the high computing cost poses a challenge to its widespread application. The present study aims to construct DeepCSP, a feasible pure machine learning framework for minute-scale rapid organic CSP. Initially, based on 177,746 data entries from the Cambridge Crystal Structure Database, a generative adversarial network was built to conditionally generate trial crystal structures under selected feature constraints for the given molecule. Simultaneously, a graph convolutional attention network was used to predict the density of stable crystal structures for the input molecule. Subsequently, the distances between the predicted density and the definition-based calculated density would be considered to be the crystal structure screening and ranking basis, and finally, the density-based crystal structure ranking would be output. Two such distinct algorithms, performing the generation and ranking functionalities, respectively, collectively constitute the DeepCSP, which has demonstrated compelling performance in marketed drug validations, achieving an accuracy rate exceeding 80% and a hit rate surpassing 85%. Inspiringly, the computing speed of the pure machine learning methodology demonstrates the potential of artificial intelligence in advancing CSP research

Ginzburg–Landau Analysis on the Physical Properties of the Kagome Superconductor CsV₃Sb₅

The kagome lattice consisting of corner-sharing triangles has been studied in the context of quantum physics for more than seventy years. For the novel discovered kagome superconductor CsV3Sb5, identifying the pairing symmetry of order parameter remained an elusive problem until now. Based on the two-band Ginzburg–Landau theory, we study the temperature dependence of upper critical field and magnetic penetration depth for this compound. All theoretical results are consistent with the experimental data, which strongly indicates the existence of two-gap s-wave superconductivity in this system. In addition, it is worth noting that the anisotropy of effective masses in the band with large (or small) gap is about 70 (or 2.4). With the calculation of the Kadowaki–Woods ratio as 0.58×10−5μΩ cm mol2 K2 mJ−2, the semi-heavy-fermion feature is suggested in the compound CsV3Sb5