144 research outputs found
Particle approximation for Lagrangian Stochastic Models with specular boundary condition
In this paper, we prove a particle approximation, in the sense of the
propagation of chaos, of a Lagrangian stochastic model submitted to specular
boundary condition and satisfying the mean no-permeability condition
Clarification and complement to "Mean-field description and propagation of chaos in networks of Hodgkin-Huxley and FitzHugh-Nagumo neurons"
In this note, we clarify the well-posedness of the limit equations to the
mean-field -neuron models proposed in Baladron et al. and we prove the
associated propagation of chaos property. We also complete the modeling issue
in Baladron et al. by discussing the well-posedness of the stochastic
differential equations which govern the behavior of the ion channels and the
amount of available neurotransmitters
Nash equilibrium for coupling of CO2 allowances and electricity markets
In this note, we present an existence result of a Nash equilibrium between
electricity producers selling their production on an electricity market and
buying CO2 emission allowances on an auction carbon market. The producers'
strategies integrate the coupling of the two markets via the cost functions of
the electricity production. We set out a clear Nash equilibrium that can be
used to compute equilibrium prices on both markets as well as the related
electricity produced and CO2 emissions covered
Game theory analysis for carbon auction market through electricity market coupling
In this paper, we analyze Nash equilibria between electricity producers
selling their production on an electricity market and buying CO2 emission
allowances on an auction carbon market. The producers' strategies integrate the
coupling of the two markets via the cost functions of the electricity
production. We set out a clear Nash equilibrium on the power market that can be
used to compute equilibrium prices on both markets as well as the related
electricity produced and CO2 emissions released.Comment: arXiv admin note: text overlap with arXiv:1311.153
Modeling the wind circulation around mills with a Lagrangian stochastic approach
This work aims at introducing model methodology and numerical studies related
to a Lagrangian stochastic approach applied to the computation of the wind
circulation around mills. We adapt the Lagrangian stochastic downscaling method
that we have introduced in [3] and [4] to the atmospheric boundary layer and we
introduce here a Lagrangian version of the actuator disc methods to take
account of the mills. We present our numerical method and numerical experiments
in the case of non rotating and rotating actuator disc models. We also present
some features of our numerical method, in particular the computation of the
probability distribution of the wind in the wake zone, as a byproduct of the
fluid particle model and the associated PDF method
Optimal Rate of Convergence of a Stochastic Particle Method to Solutions of 1D Viscous Scalar Conservation Law Equations
The aim of this work is to present the analysis of the rate of convergence of a stochastic particle method for 1D viscous scalar conservation law equations. The convergence rate result is \mathcal O(\D t + 1/\sqrt{N}), where is the number of numerical particles and \D t is the time step of the first order Euler scheme applied to the dynamic of the interacting particles
Analyzing the Applicability of Random Forest-Based Models for the Forecast of Run-of-River Hydropower Generation
ABSTRACT: Analyzing the impact of climate variables into the operational planning processes is essential for the robust implementation of a sustainable power system. This paper deals with the modeling of the run-of-river hydropower production based on climate variables on the European scale. A better understanding of future run-of-river generation patterns has important implications for power systems with increasing shares of solar and wind power. Run-of-river plants are less intermittent than solar or wind but also less dispatchable than dams with storage capacity. However, translating time series of climate data (precipitation and air temperature) into time series of run-of-river-based hydropower generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. This task is also more complex when performed for a large interconnected area. In this work, a model is built for several European countries by using machine learning techniques. In particular, we compare the accuracy of models based on the Random Forest algorithm and show that a more accurate model is obtained when a finer spatial resolution of climate data is introduced. We then discuss the practical applicability of a machine learning model for the medium term forecasts and show that some very context specific but influential events are hard to capture.info:eu-repo/semantics/publishedVersio
Stochastic model for the alignment and tumbling of rigid fibres in two-dimensional turbulent shear flow
Non-spherical particles transported by an anisotropic turbulent flow
preferentially align with the mean shear and intermittently tumble when the
local strain fluctuates. Such an intricate behaviour is here studied for
inertialess, rod-shaped particles embedded in a two-dimensional turbulent flow
with homogeneous shear. A Lagrangian stochastic model for the rods angular
dynamics is introduced and compared to the results of direct numerical
simulations. The model consists in superposing a short-correlated random
component to the steady large-scale mean shear, and can thereby be integrated
analytically. To reproduce the single-time orientation statistics obtained
numerically, it is found that one has to properly account for the combined
effect of the mean shear, for anisotropic velocity gradient fluctuations, and
for the presence of persistent rotating structures in the flow that bias
Lagrangian statistics. The model is then used to address two-time statistics.
The notion of tumbling rate is extended to diffusive dynamics by introducing
the stationary probability flux of the rods unfolded angle. The model is found
to reproduce the long-term effects of an average shear on the mean and the
variance of the fibres angular increment. Still, it does not reproduce an
intricate behaviour observed in numerics for intermediate times: the unfolded
angle is there very similar to a L\'evy walk with distributions of increments
displaying intermediate power-law tails
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