Increasing thermal plant flexibility in a high renewables power system

Thermal generation is a vital component of mature and reliable electricity markets. As the share of renewable electricity in such markets grows, so too do the challenges associated with its variability. Proposed solutions to these challenges typically focus on alternatives to primary generation, such as energy storage, demand side management, or increased interconnection. Less attention is given to the demands placed on conventional thermal generation or its potential for increased flexibility. However, for the foreseeable future, conventional plants will have to operate alongside new renewables and have an essential role in accommodating increasing supply-side variability. This paper explores the role that conventional generation has to play in managing variability through the sub-system case study of Northern Ireland, identifying the significance of specific plant characteristics for reliable system operation. Particular attention is given to the challenges of wind ramping and the need to avoid excessive wind curtailment. Potential for conflict is identified with the role for conventional plant in addressing these two challenges. Market specific strategies for using the existing fleet of generation to reduce the impact of renewable resource variability are proposed, and wider lessons from the approach taken are identified.

The impact of monsoon intraseasonal variability on renewable power generation in India

India is increasingly investing in renewable technology to meet rising energy demands, with hydropower and other renewables comprising one-third of current installed capacity. Installed wind-power is projected to increase 5-fold by 2035 (to nearly 100GW) under the International Energy Agency’s New Policies scenario. However, renewable electricity generation is dependent upon the prevailing meteorology, which is strongly influenced by monsoon variability. Prosperity and widespread electrification are increasing the demand for air conditioning, especially during the warm summer. This study uses multi-decadal observations and meteorological reanalysis data to assess the impact of intraseasonal monsoon variability on the balance of electricity supply from wind-power and temperature-related demand in India. Active monsoon phases are characterised by vigorous convection and heavy rainfall over central India. This results in lower temperatures giving lower cooling energy demand, while strong westerly winds yield high wind-power output. In contrast, monsoon breaks are characterised by suppressed precipitation, with higher temperatures and hence greater demand for cooling, and lower wind-power output across much of India. The opposing relationship between wind-power supply and cooling demand during active phases (low demand, high supply) and breaks (high demand, low supply) suggests that monsoon variability will tend to exacerbate fluctuations in the so-called demand-net-wind (i.e., electrical demand that must be supplied from non-wind sources). This study may have important implications for the design of power systems and for investment decisions in conventional schedulable generation facilities (such as coal and gas) that are used to maintain the supply/demand balance. In particular, if it is assumed (as is common) that the generated wind-power operates as a price-taker (i.e., wind farm operators always wish to sell their power, irrespective of price) then investors in conventional facilities will face additional weather-volatility through the monsoonal impact on the length and frequency of production periods (i.e. their load-duration curves).

Vibration-rotation bands of diazomethane

Some absorption bands of diazomethane vapour between 1950-3500 cm-1 have been measured with very high resolving power. The rotational structure of two parallel bands and of one perpendicular band has been resolved, and approximate values have been determined for the rotational constants. The results are consistent with the geometrical structure usually accepted for this molecule. A peculiarity in the results for the band near 2100 cm-1, together with other facts, leads to the suggestion that a tautomeric form of this molecule exists, HCN=NH, being an isoelectronic analogue of hydrazoic acid.

A double data rate (DDR) architecture for OFDM based wireless consumer devices

The creation of OFDM based Wireless Personal Area Networks (WPANs) has allowed the development of high bit-rate wireless communication devices suitable for streaming High Definition video between consumer products, as demonstrated in Wireless-USB and Wireless-HDMI. However, these devices need high frequency clock rates, particularly for the OFDM, FFT and symbol processing sections resulting in high silicon cost and high electrical power. The high clock rates make hardware prototyping difficult and verification is therefore very important but costly. Acknowledging that electrical power in wireless consumer devices is more critical than the number of implemented logic gates, this paper presents a Double Data Rate (DDR) architecture for implementation inside a OFDM baseband codec in order to reduce the high frequency clock rates by a complete factor of 2. The presented architecture has been implemented and tested for ECMA-368 (Wireless- USB context) resulting in a maximum clock rate of 264MHz instead of the expected 528MHz clock rate existing anywhere on the baseband codec die.

A double data rate (DDR) architecture for OFDM based wireless consumer devices

The creation of OFDM based Wireless Personal Area Networks (WPANs) has allowed high bit-rate wireless communication devices suitable for streaming High Definition video between consumer products as demonstrated in Wireless- USB. However, these devices need high clock rates, particularly for the OFDM sections resulting in high silicon cost and high electrical power. Acknowledging that electrical power in wireless consumer devices is more critical than the number of implemented logic gates, this paper presents a Double Data Rate (DDR) architecture to reduce the OFDM input and output clock rate by a factor of 2. The architecture has been implemented and tested for Wireless-USB (ECMA-368) resulting in a maximum clock of 264MHz instead of 528MHz existing anywhere on the die.

The fundamental vibration-rotation bands of 13C16O and 12C18O

Rotation lines in the fundamental vibration bands of 13C16O and 12C180 have been measured, using very high resolving power and more accurate wavelength calibrations than previously. The molecular rotational and vibrational constants have been deduced and compared in relation to the mass differences between these molecules and the main species 12C160.

Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

Liquid chromatography-mass spectrometry (LC-MS) datasets can be compared or combined following chromatographic alignment. Here we describe a simple solution to the specific problem of aligning one LC-MS dataset and one LC-MS/MS dataset, acquired on separate instruments from an enzymatic digest of a protein mixture, using feature extraction and a genetic algorithm. First, the LC-MS dataset is searched within a few ppm of the calculated theoretical masses of peptides confidently identified by LC-MS/MS. A piecewise linear function is then fitted to these matched peptides using a genetic algorithm with a fitness function that is insensitive to incorrect matches but sufficiently flexible to adapt to the discrete shifts common when comparing LC datasets. We demonstrate the utility of this method by aligning ion trap LC-MS/MS data with accurate LC-MS data from an FTICR mass spectrometer and show how hybrid datasets can improve peptide and protein identification by combining the speed of the ion trap with the mass accuracy of the FTICR, similar to using a hybrid ion trap-FTICR instrument. We also show that the high resolving power of FTICR can improve precision and linear dynamic range in quantitative proteomics. The alignment software, msalign, is freely available as open source.

Analytical calculation of resonant inductance for zero voltage switching in phase-shifted full-bridge converters

The phase shift full bridge (PSFB) converter allows high efficiency power conversion at high frequencies through zero voltage switching (ZVS); the parasitic drain-to-source capacitance of the MOSFET is discharged by a resonant inductance before the switch is gated resulting in near zero turn-on switching losses. Typically, an extra inductance is added to the leakage inductance of a transformer to form the resonant inductance necessary to charge and discharge the parasitic capacitances of the PSFB converter. However, many PSFB models do not consider the effects of the magnetizing inductance or dead-time in selecting the resonant inductance required to achieve ZVS. The choice of resonant inductance is crucial to the ZVS operation of the PSFB converter. Incorrectly sized resonant inductance will not achieve ZVS or will limit the load regulation ability of the converter. This paper presents a unique and accurate equation for calculating the resonant inductance required to achieve ZVS over a wide load range incorporating the effects of the magnetizing inductance and dead-time. The derived equations are validated against PSPICE simulations of a PSFB converter and extensive hardware experimentations.

Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.

Urban economic openness and IPO underpricing

We examine the empirical impact of trade openness on the short-run underpricing of initial public offerings (IPOs) using city-level real estate data. This paper represents a first attempt to employ a macroeconomic approach to explain IPO performance. We investigate an openness effect in which urban economic openness (UEO) has a significant impact on the productivity and on the prices of both direct and indirect real estate due to productivity gains of companies in more open areas. This in turn positively affects the firm’s profitability, enhancing the confidence in the local real estate market and the future company performance and decreasing the uncertainty of the IPO valuation. And as a result, we find that issuers have less incentive to underprice the IPO shares. China provides a suitable experimental ground to study the immense underpricing in developing markets, which cannot solely be accounted for by firm specific effects. First, Chinese real estate companies show strong geographic patterns focusing their businesses locally – usually at a city level. Second, we observe a degree of openness which is significantly heterogeneous across Chinese cities. Controlling for company-specific variables, location and state ownership, we find the evidence that companies whose businesses are in economically more open areas experience less IPO underpricing. Our results show high explanatory power and are robust to diverse specifications.

Adaptive B-spline neural network based nonlinear equalization for high-order QAM systems with nonlinear transmit high power amplifier

High bandwidth-efficiency quadrature amplitude modulation (QAM) signaling widely adopted in high-rate communication systems suffers from a drawback of high peak-toaverage power ratio, which may cause the nonlinear saturation of the high power amplifier (HPA) at transmitter. Thus, practical high-throughput QAM communication systems exhibit nonlinear and dispersive channel characteristics that must be modeled as a Hammerstein channel. Standard linear equalization becomes inadequate for such Hammerstein communication systems. In this paper, we advocate an adaptive B-Spline neural network based nonlinear equalizer. Specifically, during the training phase, an efficient alternating least squares (LS) scheme is employed to estimate the parameters of the Hammerstein channel, including both the channel impulse response (CIR) coefficients and the parameters of the B-spline neural network that models the HPA’s nonlinearity. In addition, another B-spline neural network is used to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can easily be estimated using the standard LS algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. Nonlinear equalisation of the Hammerstein channel is then accomplished by the linear equalization based on the estimated CIR as well as the inverse B-spline neural network model. Furthermore, during the data communication phase, the decision-directed LS channel estimation is adopted to track the time-varying CIR. Extensive simulation results demonstrate the effectiveness of our proposed B-Spline neural network based nonlinear equalization scheme.