Design Day Analysis - Forecasting Extreme Daily Natural Gas Demand

This work provides a framework for Design Day analysis. First, we estimate the temperature conditions which are expected to be colder than all but one day in N years. This temperature is known as the Design Day condition. Then, we forecast an upper bound on natural gas demand when temperature is at the Design Day condition. Natural gas distribution companies (LDCs) need to meet demand during extreme cold days. Just as bridge builders design for a nominal load, natural gas distribution companies need to design for a nominal temperature. This nominal temperature is the Design Day condition. The Design Day condition is the temperature that is expected to be colder than every day except one in N years. Once Design Day conditions are estimated, LDCs need to prepare for the Design Day demand. We provide an upper bound on Design Day demand to ensure LDCs will be able to meet demand. Design Day conditions are determined in a variety of ways. First, we fit a kernel density function to surrogate temperatures - this method is referred to as the Surrogate Kernel Density Fit. Second, we apply Extreme Value Theory - a field dedicated to finding the maxima or minima of a distribution. In particular, we apply Block-Maxima and Peak-Over-Threshold (POT) techniques. The upper bound of Design Day demand is determined using a modified version of quantile regression. Similar Design Day conditions are estimated by both the Surrogate Kernel Density Fit and Peaks-Over-Threshold methods. Both methods perform well. The theory supporting the POT method and the empirical performance of the SKDF method lends confidence in the Design Day conditions estimates. The upper bound of demand on these conditions is well modeled by the modified quantile regression technique

Forecasting Design Day Demand Using Extremal Quantile Regression

Extreme events occur rarely, making them difficult to predict. Extreme cold events strain natural gas systems to their limits. Natural gas distribution companies need to be prepared to satisfy demand on any given day that is at or warmer than an extreme cold threshold. The hypothetical day with temperature at this threshold is called the Design Day. To guarantee Design Day demand is satisfied, distribution companies need to determine the demand that is unlikely to be exceeded on the Design Day. We approach determining this demand as an extremal quantile regression problem. We review current methods for extremal quantile regression. We implement a quantile forecast to estimate the demand that has a minimal chance of being exceeded on the design day. We show extremal quantile regression to be more reliable than direct quantile estimation. We discuss the difficult task of evaluating a probabilistic forecast on rare events. Probabilistic forecasting is a quickly growing research topic in the field of energy forecasting. Our paper contributes to this field in three ways. First, we forecast quantiles during extreme cold events where data is sparse. Second, we forecast extremely high quantiles that have a very low probability of being exceeded. Finally, we provide a real world scenario on which to apply these techniques

Mathematical modeling of thermal power plant's boiler air-gas flow path regulation modes

В работе предлагаются математические модели газовоздушного тракта котла и механизмов собственных нужд ТЭС. С использованием табличных и графических представлений напорных характеристик серийных вентиляторов и дымососов получены эквивалентные соотношения для сети механизмов. Исследована задача нахождения оптимальных параметров управления для группы центробежных механизмов, обеспечивающих работу газовоздушного тракта котла. Исследовано влияние разрежения в топке котла на режим работы его вспомогательных механизмов. Приводятся результаты моделирования для типичных последовательно-параллельных соединений механизмов в гидравлических сетях ТЭС.The paper presents a mathematical model for thermal power plant's boiler air-gas flow paths and auxiliaries. With application of production fans' and flue gas extractor fans' head-capacity curves and tables, equivalent relations for the net of the mechanisms are obtained. A problem of determining the optimal control parameters for a group of centrifugal mechanisms in the air-gas path is studied. The effect of the boiler furnace draft on its auxiliaries operation is analyzed. The results of mathematical modeling for typical serial and parallel connections of the mechanisms in the thermal power plant hydraulic network are given

2.4-Å structure of the double-ring Gemmatimonas phototrophica photosystem.

Phototrophic Gemmatimonadetes evolved the ability to use solar energy following horizontal transfer of photosynthesis-related genes from an ancient phototrophic proteobacterium. The electron cryo-microscopy structure of the Gemmatimonas phototrophica photosystem at 2.4 Å reveals a unique, double-ring complex. Two unique membrane-extrinsic polypeptides, RC-S and RC-U, hold the central type 2 reaction center (RC) within an inner 16-subunit light-harvesting 1 (LH1) ring, which is encircled by an outer 24-subunit antenna ring (LHh) that adds light-gathering capacity. Femtosecond kinetics reveal the flow of energy within the RC-dLH complex, from the outer LHh ring to LH1 and then to the RC. This structural and functional study shows that G. phototrophica has independently evolved its own compact, robust, and highly effective architecture for harvesting and trapping solar energy

2.4-Å structure of the double-ring Gemmatimonas phototrophica photosystem.

Phototrophic Gemmatimonadetes evolved the ability to use solar energy following horizontal transfer of photosynthesis-related genes from an ancient phototrophic proteobacterium. The electron cryo-microscopy structure of the Gemmatimonas phototrophica photosystem at 2.4 Å reveals a unique, double-ring complex. Two unique membrane-extrinsic polypeptides, RC-S and RC-U, hold the central type 2 reaction center (RC) within an inner 16-subunit light-harvesting 1 (LH1) ring, which is encircled by an outer 24-subunit antenna ring (LHh) that adds light-gathering capacity. Femtosecond kinetics reveal the flow of energy within the RC-dLH complex, from the outer LHh ring to LH1 and then to the RC. This structural and functional study shows that G. phototrophica has independently evolved its own compact, robust, and highly effective architecture for harvesting and trapping solar energy

A Surrogate Weather Generator for Estimating Natural Gas Design Day Conditions

Natural gas customers rely upon utilities to provide gas for heating in the coldest parts of winter. Heating capacity is expensive, so utilities and end users (represented by commissions) must agree on the coldest day on which a utility is expected to meet demand. The return period of such a day is long relative to the amount of weather data that are typically available. This paper develops a weather resampling method called the Surrogate Weather Resampler, which creates a large dataset to support analysis of extremely infrequent events. While most current methods for generating weather data are based on simulation, this method resamples the deviations from typical weather. The paper also shows how extreme temperatures are strongly correlated to the demand for natural gas. The Surrogate Weather Resampler was compared in-sample and out-of-sample to the WeaGETS weather generator using both the Kolmogorov–Smirnov test and an exceedance-based test for cold weather generation. A naïve benchmark was also examined. These methods studied weather data from the National Oceanic and Atmospheric Administration and AccuWeather. Weather data were collected for 33 weather stations across North America, with 69 years of data from each weather station. We show that the Surrogate Weather Resampler can reproduce the cold tail of distribution better than the naïve benchmark and WeaGETS

Vysoce definované a velikostně selektivní nanášení nanočástic pomocí manipulace v elektrostatickém poli

Tato studie kombinuje numerickou simulaci s experimentálním výzkumem nanostrukturovaných povrchů na vysoké úrovni vytvořených z kovových nanočástic (NP). Konečným cílem tohoto výzkumu je připravit vysoce designované izotropní povrchy s vysokou čistotou, definovanou velikostí, homogenním pokrytím povrchu a vysokou rychlostí depozice, tj. vlastnosti oceňované např. v precizním nanoinženýrství mikrosenzorů a/nebo nanoelektroniky apod. Přístup je založen na manipulaci s elektricky nabitými nanočásticemi, produkovanými zdrojem klastrů s agregací v plynné fázi (GAS), v elektrostatickém poli tvořeném dvěma paralelními elektrodami umístěnými u výstupního otvoru GAS. Model umožňuje předpovídat rozložení a vlastnosti NP napříč rovinou (x, y) substrátu jako funkci elektrického potenciálu superponovaného nabitými NP v objemu a vnějšího elektrického pole. Byla zjištěna dobrá kvalitativní shoda mezi modelem a vlastnostmi NP deponovaných pomocí GAS na Si substrát (deponovaný povrch byl analyzován zobrazením pomocí mikroskopie atomárních sil (AFM)), což ukazuje elektrostatickou manipulaci se svazkem nabitých NP jako slibný nástroj pro depozici vysoce přizpůsobených nanostruktur.This study combines numerical simulation with an experimental investigation of high-level designed nanostructured surfaces built from metal nanoparticles (NPs). The ultimate goal of this research is to prepare highly designed isotropic surfaces with high purity, defined size, homogeneous surface coverage, and high deposition rate, i.e., the properties appreciated e.g., in precise nanoengineering of microsensors and/or nanoelectronics, etc. The approach is based on the manipulation of electrically charged nanoparticles, produced by a gas aggregation cluster source (GAS), in the electrostatic field propagated by two parallel electrodes mounted at the GAS output orifice. The model derived by numerical simulation enables prediction of the distribution and properties of the NPs across the (x, y) substrate plane as a function of the electrical potential superimposed by the charged NPs in the volume and the external electrical field. A good qualitative agreement was found between the model and the properties of GAS-deposited NPs onto the Si substrate (the deposited surface was analyzed by the atomic force microscopy (AFM) imaging), which reveals the electrostatic manipulation of the charged NPs beam as a promising tool for deposition of highly tailored nanostructures