Challenges of Primary Frequency Control and Benefits of Primary Frequency Response Support from Electric Vehicles

As the integration of wind generation displaces conventional plants, system inertia provided by rotating mass declines, causing concerns over system frequency stability. This paper implements an advanced stochastic scheduling model with inertia-dependent fast frequency response requirements to investigate the challenges on the primary frequency control in the future Great Britain electricity system. The results suggest that the required volume and the associated cost of primary frequency response increase significantly along with the increased capacity of wind plants. Alternative measures (e.g. electric vehicles) have been proposed to alleviate these concerns. Therefore, this paper also analyses the benefits of primary frequency response support from electric vehicles in reducing system operation cost, wind curtailment and carbon emissions

Benchmarking explanatory models for inertia forecasting using public data of the nordic area

This paper investigates the performance of a day-ahead explanatory model for inertia forecasting based on field data in the Nordic system, which achieves a 43% reduction in mean absolute percentage error (MAPE) against a state-of-the-art time-series forecast model. The generalizability of the explanatory model is verified by its consistent performance on Nordic and Great Britain datasets. Also, it appears that a long duration of training data is not required to obtain accurate results with this model, but taking a more spatially granular approach reduces the MAPE by 3.6%. Finally, two further model enhancements are studied considering the specific features in Nordic system: (i) a monthly interaction variable applied to the day-ahead national demand forecast feature, reducing the MAPE by up to 18%; and (ii) a feature based on the inertia from hydropower, although this has a negligible impact. The field dataset used for benchmarking is also made publicly available

Escherichia coli K1 RS218 Interacts with Human Brain Microvascular Endothelial Cells via Type 1 Fimbria Bacteria in the Fimbriated State

Escherichia coli K1 is a major gram-negative organism causing neonatal meningitis. E. coli K1 binding to and invasion of human brain microvascular endothelial cells (HBMEC) are a prerequisite for E. coli penetration into the central nervous system in vivo. In the present study, we showed using DNA microarray analysis that E. coli K1 associated with HBMEC expressed significantly higher levels of the fim genes compared to nonassociated bacteria. We also showed that E. coli K1 binding to and invasion of HBMEC were significantly decreased with its fimH deletion mutant and type 1 fimbria locked-off mutant, while they were significantly increased with its type 1 fimbria locked-on mutant. E. coli K1 strains associated with HBMEC were predominantly type 1 fimbria phase-on (i.e., fimbriated) bacteria. Taken together, we showed for the first time that type 1 fimbriae play an important role in E. coli K1 binding to and invasion of HBMEC and that type 1 fimbria phase-on E. coli is the major population interacting with HBMEC

Asymmetrical pMUTs for Focused Acoustic Pressure by Reinforcement Learning

To increase the energy utilization of a pMUT array, an advanced design scheme for asymmetrical piezoelectric micromachined ultrasonic transducers (pMUTs) has been developed with focused acoustic pressure via the deep deterministic policy gradient (DDPG) algorithm. Three distinctive accomplishments have been achieved in: 1) a highly-efficient interface platform between Python and COMSOL for asymmetry factor (AF) simulations; 2) fast freeform pMUT designs without the initial dataset; and 3) superior designs with increased 34% pressure outputs for potential applications such as contact-less haptics. As such, the proposed design scheme could be applied to other MEMS devices to improve system efficiency

Charging load pattern extraction for residential electric vehicles: a training-free nonintrusive method

Extracting the charging load pattern of residential electric vehicle (REV) will help grid operators make informed decisions in terms of scheduling and demand-side response management. Due to the multistate and high-frequency characteristics of integrated residential appliances from the residential perspective, it is difficult to achieve accurate extraction of the charging load pattern. To deal with that, this article presents a novel charging load extraction method based on residential smart meter data to noninvasively extract REV charging load pattern. The proposed algorithm harnesses the low-frequency characteristics of the charging load pattern and applies a two-stage decomposition technique to extract the characteristics of the charging load. The two-stage decomposition technique mainly includes: the trend component of the charging load being decomposed by seasonal and trend decomposition using loess method, and the low-frequency approximate component being decomposed by discrete wavelet technology. Furthermore, based on the extracted characteristics, event monitoring, and dynamic time warping is applied to estimate the closest charging interval and amplitude. The key features of the proposed algorithm include 1) significant improvement in extraction accuracy; 2) strong noise immunity; 3) online implementation of extraction. Experiments based on ground truth data validate the superiority of the proposed method compared to the existing ones

Quark Delocalization, Color Screening, and Nuclear Intermediate Range Attraction

We consider the effect of including quark delocalization and color screening, in the nonrelativistic quark cluster model, on baryon-baryon potentials and phase shifts. We find that the inclusion of these additional effects allows a good qualitative description of both.Comment: 10 pages, LaTeX, 4 figures in PostScript after text, LA-UR-91-215