Relativistic breather-type solitary waves with linear polarization in cold plasmas

Linearly polarized solitary waves, arising from the interaction of an intense laser pulse with a plasma, are investigated. Localized structures, in the form of exact numerical nonlinear solutions of the one-dimensional Maxwell-fluid model for a cold plasma with fixed ions, are presented. Unlike stationary circularly polarized solitary waves, the linear polarization gives rise to a breather-type behavior and a periodic exchange of electromagnetic energy and electron kinetic energy at twice the frequency of the wave. A numerical method based on a finite-differences scheme allows us to compute a branch of solutions within the frequency range Ωmin<Ω<ωpe, where ωpe and Ωmin are the electron plasma frequency and the frequency value for which the plasma density vanishes locally, respectively. A detailed description of the spatiotemporal structure of the waves and their main properties as a function of Ω is presented. Small-amplitude oscillations appearing in the tail of the solitary waves, a consequence of the linear polarization and harmonic excitation, are explained with the aid of the Akhiezer-Polovin system. Direct numerical simulations of the Maxwell-fluid model show that these solitary waves propagate without change for a long time.

Amplitude modulation of quantum-ion-acoustic wavepackets in electron-positron-ion plasmas: Modulational instability, envelope modes, extreme waves

A semirelativistic fluid model is employed to describe the nonlinear amplitude modulation of low-frequency (ionic scale) electrostatic waves in an unmagnetized electron-positron-ion plasma. Electrons and positrons are assumed to be degenerated and inertialess, whereas ions are warm and classical. A multiscale perturbation method is used to derive a nonlinear Schrödinger equation for the envelope amplitude, based on which the occurrence of modulational instability is investigated in detail. Various types of localized ion acoustic excitations are shown to exist, in the form of either bright type envelope solitons (envelope pulses) or dark-type envelope solitons (voids, holes). The plasma configurational parameters (namely, the relativistic degeneracy parameter, the positron concentration, and the ionic temperature) are shown to affect the conditions for modulational instability significantly, in fact modifying the associated threshold as well as the instability growth rate. In particular, the relativistic degeneracy parameter leads to an enhancement of the modulational instability mechanism. Furthermore, the effect of different relevant plasma parameters on the characteristics (amplitude, width) of these envelope solitary structures is also presented in detail. Finally, the occurrence of extreme amplitude excitation (rogue waves) is also discussed briefly. Our results aim at elucidating the formation and dynamics of nonlinear electrostatic excitations in superdense astrophysical regimes.

Impact of electric vehicles on a carbon constrained power system—a post 2020 case study

Electric vehicles (EVs) offer great potential to move from fossil fuel dependency in transport once some of the technical barriers related to battery reliability and grid integration are resolved. The European Union has set a target to achieve a 10% reduction in greenhouse gas emissions by 2020 relative to 2005 levels. This target is binding in all the European Union member states. If electric vehicle issues are overcome then the challenge is to use as much renewable energy as possible to achieve this target. In this paper, the impacts of electric vehicle charged in the all-Ireland single wholesale electricity market after the 2020 deadline passes is investigated using a power system dispatch model. For the purpose of this work it is assumed that a 10% electric vehicle target in the Republic of Ireland is not achieved, but instead 8% is reached by 2025 considering the slow market uptake of electric vehicles. Our experimental study shows that the increasing penetration of EVs could contribute to approach the target of the EU and Ireland government on emissions reduction, regardless of different charging scenarios. Furthermore, among various charging scenarios, the off-peak charging is the best approach, contributing 2.07% to the target of 10% reduction of Greenhouse gas emissions by 2025.

Non-convex dynamic economic/environmental dispatch with plug-in electric vehicle loads

Electric vehicles are a key prospect for future transportation. A large penetration of electric vehicles has the potential to reduce the global fossil fuel consumption and hence the greenhouse gas emissions and air pollution. However, the additional stochastic loads imposed by plug-in electric vehicles will possibly introduce significant changes to existing load profiles. In his paper, electric vehicles loads are integrated into an 5-unit system using a non-convex dynamic dispatch model. The actual infrastructure characteristics including valve-point effects, load balance constrains and transmission loss have been included in the model. Multiple load profiles are comparatively studied and compared in terms of economic and environmental impacts in order o identify patterns to charge properly. The study as expected shows ha off-peak charging is the best scenario with respect to using less fuels and producing less emissions.

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.