Distribution network demand and its uncertainty

This chapter presents some advanced tools for low voltage (LV) network demand simulation. Such methods will be required to help distribution network operators (DNOs) cope with the increased uptake of low carbon technologies and localised sources of generation. This will enable DNOs to manage the current network, simulate the effect of various scenarios and run load flow analysis. In order to implement such analysis requires high resolution smart meter data for the various customers connected to the network. However, only small amounts of individual smart meter data will be available and such data could be expensive. In likelihood, smart meter data is only going to be freely available at the aggregate level. Hence, in general, to implement LV network tools, customer loads will need to be simulated based on the assumption of limited amounts of monitored data. In addition, due to the high volatility of LV electric distribution networks, demand uncertainty must also be captured within a simulation tool. In this chapter, a number of methods are described for simulating demand on low voltage feeders which rely only on relatively small samples of smart meter data and monitoring. Firstly, a method called "buddying" is described for assigning realistic profiles to unmonitored customers by buddying them to a customer who is monitored. Secondly, a number of methods are presented for capturing the uncertainty on the network. Finally the uncertainty models are incorporated into the buddying method and implemented in a load flow analysis tool on a number of real feeders. Both the buddying and the uncertainty estimation are presented for two different cases based on whether LV substation monitoring is present or not. This illustrates the different impacts of monitoring availability on the modelling tools. This chapter demonstrates the presented methods on a large range of real LV feeders

Conditioning of incremental variational data assimilation, with application to the Met Office system

Implementations of incremental variational data assimilation require the iterative minimization of a series of linear least-squares cost functions. The accuracy and speed with which these linear minimization problems can be solved is determined by the condition number of the Hessian of the problem. In this study, we examine how different components of the assimilation system influence this condition number. Theoretical bounds on the condition number for a single parameter system are presented and used to predict how the condition number is affected by the observation distribution and accuracy and by the specified lengthscales in the background error covariance matrix. The theoretical results are verified in the Met Office variational data assimilation system, using both pseudo-observations and real data

Competitive fitness of essential gene knockdowns reveals a broad-spectrum antibacterial inhibitor of the cell division protein FtsZ

To streamline the elucidation of antibacterial compounds' mechanism of action, comprehensive high-throughput assays interrogating multiple putative targets are necessary. However, current chemogenomic approaches for antibiotic target identification have not fully utilized the multiplexing potential of next-generation sequencing. Here, we used Illumina sequencing of transposon insertions to track the competitive fitness of a Burkholderia cenocepacia library containing essential gene knockdowns. Using this method, we characterized a novel benzothiadiazole derivative, 10126109 (C109), with antibacterial activity against B. cenocepacia, for which whole-genome sequencing of low-frequency spontaneous drug-resistant mutants had failed to identify the drug target. By combining the identification of hypersusceptible mutants and morphology screening, we show that C109 targets cell division. Furthermore, fluorescence microscopy of bacteria harboring green fluorescent protein (GFP) cell division protein fusions revealed that C109 prevents divisome formation by altering the localization of the essential cell division protein FtsZ. In agreement with this, C109 inhibited both the GTPase and polymerization activities of purified B. cenocepacia FtsZ. C109 displayed antibacterial activity against Gram-positive and Gram-negative cystic fibrosis pathogens, including Mycobacterium abscessus C109 effectively cleared B. cenocepacia infection in the Caenorhabditis elegans model and exhibited additive interactions with clinically relevant antibiotics. Hence, C109 is an enticing candidate for further drug development

Lightwave analog links for LHC detector frontends

The requirements on optical links for transferring analog and digital signals from the detector front-ends to the readout electronics at future high-luminosity colliders are reviewed. The advantages of external modulation techniques are discussed. An outline is given of the the R&D programme recently started at CERN by a collaboration involving high-energy physics institutes, optoelectronics research laboratories and industry, in order to develop electro-optic intensity modulator arrays, particularly for analogue applications, and to investigate the feasibility of volume production. The design of multichannel demonstrators in lithium niobate and III-V semiconductor technology is described. Preliminary results of the performance measurements are presented