Day-Ahead Optimal Charging/Discharging Scheduling for Electric Vehicles in Micro-Grids

A micro-grid is an autonomous system which can be operated and connected to an external system or isolated with the help of energy storage systems (ESSs). While the daily output of distributed generators (DGs) strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the imbalance between the daily load and generation curves. In this paper, a statistical model is presented to describe daily EV charging/discharging behaviour. An optimisation problem is proposed to obtain economic operation for the micro-grid based on this model. In day-ahead scheduling, with estimated information of power generation and load demand, optimal charging/discharging of EVs during 24 hours is obtained. A series of numerical optimization solutions in different scenarios is achieved by serial quadratic programming. The results show that optimal charging/discharging of EVs, a daily load curve can better track the generation curve and the network loss and required ESS capacity are both decreased. The paper also demonstrates cost benefits for EVs and operators.

Covariance Averaging for Improved Estimation and Portfolio Allocation

We propose a new method for estimating the covariance matrix of a multivariate time series of nancial returns. The method is based on estimating sample covariances from overlapping windows of observations which are then appropriately weighted to obtain the nal covariance estimate. We extend the idea of (model) covariance averaging o ered in the covariance shrinkage approach by means of greater ease of use, exibility and robustness in averaging information over different data segments. The suggested approach does not su er from the curse of dimensionality and can be used without problems of either approximation or any demand for numerical optimization.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Controlling the transport of an ion: classical and quantum mechanical solutions

The accurate transport of an ion over macroscopic distances represents a challenging control problem due to the different length and time scales that enter and the experimental limitations on the controls that need to be accounted for. Here, we investigate the performance of different control techniques for ion transport in state-of-the-art segmented miniaturized ion traps. We employ numerical optimization of classical trajectories and quantum wavepacket propagation as well as analytical solutions derived from invariant based inverse engineering and geometric optimal control. The applicability of each of the control methods depends on the length and time scales of the transport. Our comprehensive set of tools allows us make a number of observations. We find that accurate shuttling can be performed with operation times below the trap oscillation period. The maximum speed is limited by the maximum acceleration that can be exerted on the ion. When using controls obtained from classical dynamics for wavepacket propagation, wavepacket squeezing is the only quantum effect that comes into play for a large range of trapping parameters. We show that this can be corrected by a compensating force derived from invariant based inverse engineering, without a significant increase in the operation time.

On the application of optimal wavelet filter banks for ECG signal classification

This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.

Delineamento ótimos para modelos não-lineares: efeitos fixos e efeitos aleatórios

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Otimização de um ciclo Brayton irreversível com regeneração, inter-resfriamento e reaquecimento

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Linear parameter varying control design for rotating systems supported by journal bearings

Linear parameter varying (LPV) control is a model-based control technique that takes into account time-varying parameters of the plant. In the case of rotating systems supported by lubricated bearings, the dynamic characteristics of the bearings change in time as a function of the rotating speed. Hence, LPV control can tackle the problem of run-up and run-down operational conditions when dynamic characteristics of the rotating system change significantly in time due to the bearings and high vibration levels occur. In this work, the LPV control design for a flexible shaft supported by plain journal bearings is presented. The model used in the LPV control design is updated from unbalance response experimental results and dynamic coefficients for the entire range of rotating speeds are obtained by numerical optimization. Experimental implementation of the designed LPV control resulted in strong reduction of vibration amplitudes when crossing the critical speed, without affecting system behavior in sub- or supercritical speeds. (C) 2012 Elsevier Ltd. All rights reserved.

Patient-specific spinal stiffness in AIS: a preoperative and noninvasive method.

INTRODUCTION The clinical tests currently used to assess spinal biomechanics preoperatively are unable to assess true mechanical spinal stiffness. They rely on spinal displacement without considering the force required to deform a patient's spine. We propose a preoperative method for noninvasively quantifying the three-dimensional patient-specific stiffness of the spines of adolescent idiopathic scoliosis patients. METHODS The technique combines a novel clinical test with numerical optimization of a finite element model of the patient's spine. RESULTS A pilot study conducted on five patients showed that the model was able to provide accurate 3D reconstruction of the spine's midline and predict the spine's stiffness for each patient in flexion, bending, and rotation. Statistically significant variation of spinal stiffness was observed between the patients. CONCLUSION This result confirms that spinal biomechanics is patient-specific, which should be taken into consideration to individualize surgical treatment.

Desenvolvimento de algoritmo de controle de tração para regeneração de energia metroviária - ACTREM: melhoria da eficiência energética de sistemas de tração metroviária.

Esta tese propõe um modelo de regeneração de energia metroviária, baseado no controle de paradas e partidas do trem ao longo de sua viagem, com o aproveitamento da energia proveniente da frenagem regenerativa no sistema de tração. O objetivo é otimizar o consumo de energia, promover maior eficiência, na perspectiva de uma gestão sustentável. Aplicando o Algoritmo Genético (GA) para obter a melhor configuração de tráfego dos trens, a pesquisa desenvolve e testa o Algoritmo de Controle de Tração para Regeneração de Energia Metroviária (ACTREM), usando a Linguagem de programação C++. Para analisar o desempenho do algoritmo de controle ACTREM no aumento da eficiência energética, foram realizadas quinze simulações da aplicação do ACTREM na linha 4 - Amarela do metrô da cidade de São Paulo. Essas simulações demonstraram a eficiência do ACTREM para gerar, automaticamente, os diagramas horários otimizados para uma economia de energia nos sistemas metroviários, levando em consideração as restrições operacionais do sistema, como capacidade máxima de cada trem, tempo total de espera, tempo total de viagem e intervalo entre trens. Os resultados mostram que o algoritmo proposto pode economizar 9,5% da energia e não provocar impactos relevantes na capacidade de transporte de passageiros do sistema. Ainda sugerem possíveis continuidades de estudos.

Feasibility of soliton-like DPSK transmission at 40 Gb/s with in-line semiconductor optical amplifiers

We analyze a soliton-like phase-shift keying 40-Gb/s transmission system using cascaded in-line semiconductor optical amplifiers. Numerical optimization of the proposed soliton-like regime is presented.

Feasibility of soliton-like DPSK transmission at 40 Gb/s with in-line semiconductor optical amplifiers

We analyze a soliton-like phase-shift keying 40-Gb/s transmission system using cascaded in-line semiconductor optical amplifiers. Numerical optimization of the proposed soliton-like regime is presented. © 2006 IEEE.

Effect of carrier reshaping and narrow MUX-DEMUX filtering in 0.8 bit/s/Hz WDM RZ-DPSK transmission

Effect of the carrier shape in the ultra high dense wavelength division multiplexing (WDM) return to zero differential phase shift keying (RZ-DPSK) transmission has been examined through numerical optimization of the pulse form, duty cycle and narrow multiplex/de-multiplex (MUX/DEMUX) filtering parameters. © 2007 Springer Science+Business Media, LLC.

Optical regeneration at 40 Gb/s in dispersion-managed transmission lines with in-line synchronous modulators

Numerical optimization is performed of the 40-Gb/s dispersion-managed (DM) soliton transmission system with in-line synchronous intensity modulation. Stability of DM soliton transmission results from a combined action of dispersion, nonlinearity, in-line filtering, and modulation through effective periodic bandwidth management of carrier pulses. Therefore, analysis of the multiparametric problem is typically required. A two-stage time-saving numerical optimization procedure is applied. At the first step, the regions of the stable carrier propagation are determined using theoretical models available for DM solitons, and system parameters are optimized. At the second stage, full numerical simulations are undertaken in order to verify the tolerance of optimal transmission regimes. An approach developed demonstrates feasibility of error-free transmission over 20 000 km in a transmission line composed of standard fiber and dispersion compensation fiber at 40 Gb/s.

Computation of rare transitions in the barotropic quasi-geostrophic equations

We investigate the theoretical and numerical computation of rare transitions in simple geophysical turbulent models. We consider the barotropic quasi-geostrophic and two-dimensional Navier–Stokes equations in regimes where bistability between two coexisting large-scale attractors exist. By means of large deviations and instanton theory with the use of an Onsager–Machlup path integral formalism for the transition probability, we show how one can directly compute the most probable transition path between two coexisting attractors analytically in an equilibrium (Langevin) framework and numerically otherWe adapt a class of numerical optimization algorithms known as minimum action methods to simple geophysical turbulent models. We show that by numerically minimizing an appropriate action functional in a large deviation limit, one can predict the most likely transition path for a rare transition between two states. By considering examples where theoretical predictions can be made, we show that the minimum action method successfully predicts the most likely transition path. Finally, we discuss the application and extension of such numerical optimization schemes to the computation of rare transitions observed in direct numerical simulations and experiments and to other, more complex, turbulent systems.