A novel methodology for analysis of large scale interconnected power and gas systems

The European Union continues to exert a large influence on the direction of member states energy policy. The 2020 targets for renewable energy integration have had significant impact on the operation of current power systems, forcing a rapid change from fossil fuel dominated systems to those with high levels of renewable power. Additionally, the overarching aim of an internal energy market throughout Europe has and will continue to place importance on multi-jurisdictional co-operation regarding energy supply. Combining these renewable energy and multi-jurisdictional supply goals results in a complicated multi-vector energy system, where the understanding of interactions between fossil fuels, renewable energy, interconnection and economic power system operation is increasingly important. This paper provides a novel and systematic methodology to fully understand the changing dynamics of interconnected energy systems from a gas and power perspective. A fully realistic unit commitment and economic dispatch model of the 2030 power systems in Great Britain and Ireland, combined with a representative gas transmission energy flow model is developed. The importance of multi-jurisdictional integrated energy system operation in one of the most strategically important renewable energy regions is demonstrated.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

The HARPS-N Rocky Planet Search. I. HD 219134 b: A transiting rocky planet in a multi-planet system at 6.5 pc from the Sun

We know now from radial velocity surveys and transit space missions thatplanets only a few times more massive than our Earth are frequent aroundsolar-type stars. Fundamental questions about their formation history,physical properties, internal structure, and atmosphere composition are,however, still to be solved. We present here the detection of a systemof four low-mass planets around the bright (V = 5.5) and close-by (6.5pc) star HD 219134. This is the first result of the Rocky Planet Searchprogramme with HARPS-N on the Telescopio Nazionale Galileo in La Palma.The inner planet orbits the star in 3.0935 ± 0.0003 days, on aquasi-circular orbit with a semi-major axis of 0.0382 ± 0.0003AU. Spitzer observations allowed us to detect the transit of the planetin front of the star making HD 219134 b the nearest known transitingplanet to date. From the amplitude of the radial velocity variation(2.25 ± 0.22 ms-1) and observed depth of the transit(359 ± 38 ppm), the planet mass and radius are estimated to be4.36 ± 0.44 M⊕ and 1.606 ± 0.086R⊕, leading to a mean density of 5.76 ± 1.09 gcm-3, suggesting a rocky composition. One additional planetwith minimum-mass of 2.78 ± 0.65 M⊕ moves on aclose-in, quasi-circular orbit with a period of 6.767 ± 0.004days. The third planet in the system has a period of 46.66 ± 0.08days and a minimum-mass of 8.94 ± 1.13 M⊕, at0.233 ± 0.002 AU from the star. Its eccentricity is 0.46 ±0.11. The period of this planet is close to the rotational period of thestar estimated from variations of activity indicators (42.3 ± 0.1days). The planetary origin of the signal is, however, thepreferredsolution as no indication of variation at the corresponding frequency isobserved for activity-sensitive parameters. Finally, a fourth additionallonger-period planet of mass of 71 M⊕ orbits the starin 1842 days, on an eccentric orbit (e = 0.34 ± 0.17) at adistance of 2.56 AU.The photometric time series and radial velocities used in this work areavailable in electronic form at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr(ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/584/A72