A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Short-term wind power forecasting in Portugal by neural networks and wavelet transform

This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Renewable-based generation integration in electricity markets with virtual power producers

The development of renewable energy sources and Distributed Generation (DG) of electricity is of main importance in the way towards a sustainable development. However, the management, in large scale, of these technologies is complicated because of the intermittency of primary resources (wind, sunshine, etc.) and small scale of some plants. The aggregation of DG plants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets. VPPs can ensure a secure, environmentally friendly generation and optimal management of heat, electricity and cold as well as optimal operation and maintenance of electrical equipment, including the sale of electricity in the energy market. For attaining these goals, there are important issues to deal with, such as reserve management strategies, strategies for bids formulation, the producers’ remuneration, and the producers’ characterization for coalition formation. This chapter presents the most important concepts related with renewable-based generation integration in electricity markets, using VPP paradigm. The presented case studies make use of two main computer applications:ViProd and MASCEM. ViProd simulates VPP operation, including the management of plants in operation. MASCEM is a multi-agent based electricity market simulator that supports the inclusion of VPPs in the players set.

Micro-generation Evaluation of the Zero Emissions Technologies in the Portuguese Market

Micro-generation is the small scale production of heat and/or electricity from a low carbon source and can be a powerful driver for carbon reduction, behavior change, security of supply and economic value. The energy conversion technologies can include photovoltaic panels, micro combined heat and power, micro wind, heat pumps, solar thermal systems, fuel cells and micro hydro schemes. In this paper, a small research of the availability of the conversion apparatus and the prices for the micro wind turbines and photovoltaic systems is made and a comparison between these two technologies is performed in terms of the availability of the resource and costs. An analysis of the new legal framework published in Portugal is done to realize if the incentives to individualspsila investment in sustainable and local energy production is worth for their point of view. An economic evaluation for these alternatives, accounting with the governmentpsilas incentives should lead, in most cases, into attractive return rates for the investment. Apart from the attractiveness of the investment there are though other aspects that should be taken into account and those are the benefits that these choices have to us all. The idea is that micro-generation will not only make a significant direct contribution to carbon reduction targets, it will also trigger a multiplier effect in behavior change by engaging hearts and minds, and providing more efficient use of energy by householders. The diversified profile of power generation by micro-generators, both in terms of location and timing, should reduce the impact of intermittency or plant failures with significant gains for security of supply.

Scaling law in Saddle-node bifurcations for one-dimensional maps: A complex variable approach

The study of transient dynamical phenomena near bifurcation thresholds has attracted the interest of many researchers due to the relevance of bifurcations in different physical or biological systems. In the context of saddle-node bifurcations, where two or more fixed points collide annihilating each other, it is known that the dynamics can suffer the so-called delayed transition. This phenomenon emerges when the system spends a lot of time before reaching the remaining stable equilibrium, found after the bifurcation, because of the presence of a saddle-remnant in phase space. Some works have analytically tackled this phenomenon, especially in time-continuous dynamical systems, showing that the time delay, tau, scales according to an inverse square-root power law, tau similar to (mu-mu (c) )(-1/2), as the bifurcation parameter mu, is driven further away from its critical value, mu (c) . In this work, we first characterize analytically this scaling law using complex variable techniques for a family of one-dimensional maps, called the normal form for the saddle-node bifurcation. We then apply our general analytic results to a single-species ecological model with harvesting given by a unimodal map, characterizing the delayed transition and the scaling law arising due to the constant of harvesting. For both analyzed systems, we show that the numerical results are in perfect agreement with the analytical solutions we are providing. The procedure presented in this work can be used to characterize the scaling laws of one-dimensional discrete dynamical systems with saddle-node bifurcations.

Fonte comutada de baixa tensão para utilização em supercondensadores como fonte primária de energia

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo Energia

Benthic macroinvertebrates assemblages and macrohabitat connectivity in Mediterranean-climate streams of northern California

Abstract. Drought leads to a loss of longitudinal and lateral hydrologic connectivity, which causes direct or indirect changes in stream ecosystem properties. Changes in macrohabitat availability from a rifflepool sequence to isolated pools are among the most conspicuous consequences of connectivity loss. Macroinvertebrate assemblages were compared among 3 distinct stream macrohabitats (riffles [R], pools connected to riffles [Pc], disconnected pools [Pd]) of 19 Mediterranean-climate sites in northern California to examine the influence of loss of habitat resulting from drought disturbance. At the time of sampling, 10 sites were perennial and included R and Pc macrohabitats, whereas 9 sites were intermittent and included only Pd macrohabitats. Taxa richness was more variable in Pd, and taxa richness was significantly lower in Pd than in Pc but not R. These results suggested a decline in richness between Pc and Pd that might be associated with loss of connectivity. Lower Ephemeroptera, Plecoptera, and Trichoptera (EPT) richness relative to Odonata, Coleoptera, and Heteroptera (OCH) richness was observed for Pd than R and Pc macrohabitats. Family composition was more similar between R and Pc than between R or Pc and Pd macrohabitats. This similarity may be associated with greater connectivity between R and Pc macrohabitats. Correspondence analysis indicated that macroinvertebrate composition changed along a gradient from R to Pc and Pd that was related to a perennialintermittent gradient across sites. High variability among macroinvertebrate assemblages in Pd could have been related to variability in the duration of intermittency. In cluster analysis, macroinvertebrate assemblages were grouped by macrohabitat first and then by site, suggesting that the macrohabitat filter had a greater influence on macroinvertebrate assemblages than did local site characteristics. Few taxa were found exclusively in Pc, and this macrohabitat shared numerous taxa with R and Pd, indicating that Pc may act as a bridge between R and Pd during drought. Drought is regarded as a ramp disturbance, but our results suggest that the response of macroinvertebrate assemblages to the loss of hydrological connectivity among macrohabitats is gradual, at least in Mediterranean-climate streams where drying is gradual. However, the changes may be more dramatic in arid and semiarid streams or in Mediterranean-climate streams if drying is rapid.

Wavelet Analysis in Turbulence

The objective of this thesis is to study wavelets and their role in turbulence applications. Under scrutiny in the thesis is the intermittency in turbulence models. Wavelets are used as a mathematical tool to study the intermittent activities that turbulence models produce. The first section generally introduces wavelets and wavelet transforms as a mathematical tool. Moreover, the basic properties of turbulence are discussed and classical methods for modeling turbulent flows are explained. Wavelets are implemented to model the turbulence as well as to analyze turbulent signals. The model studied here is the GOY (Gledzer 1973, Ohkitani & Yamada 1989) shell model of turbulence, which is a popular model for explaining intermittency based on the cascade of kinetic energy. The goal is to introduce better quantification method for intermittency obtained in a shell model. Wavelets are localized in both space (time) and scale, therefore, they are suitable candidates for the study of singular bursts, that interrupt the calm periods of an energy flow through various scales. The study concerns two questions, namely the frequency of the occurrence as well as the intensity of the singular bursts at various Reynolds numbers. The results gave an insight that singularities become more local as Reynolds number increases. The singularities become more local also when the shell number is increased at certain Reynolds number. The study revealed that the singular bursts are more frequent at Re ~ 107 than other cases with lower Re. The intermittency of bursts for the cases with Re ~ 106 and Re ~ 105 was similar, but for the case with Re ~ 104 bursts occured after long waiting time in a different fashion so that it could not be scaled with higher Re.

The dynamics of biofilm bacterial communities is driven by flow wax and wane in a temporary stream

Biofilm communities are exposed to long periods of desiccation in temporary streams. We investigated how water flow intermittency affected the bacterial community structure colonizing three different streambed compartments in a Mediterranean stream. Massive parallel sequencing revealed different bacterial communities in biofilms from sand sediments and cobbles. Bacterial communities were similar (62% of shared operational taxonomic units) in the epipsammic and hyporheic biofilms, and more diverse than those in the epilithic biofilms. The non-flow phase caused a decrease of bacterial diversity in the biofilms, when communities included only bacterial taxa assumed to be adapted to water stress. The most sensitive bacterial communities to flow intermittency were in the epilithic, where the exposure to physical stress was the highest. In sand sediments a wide group of bacterial taxa was tolerant to desiccation. During non-flow the proliferation of opportunistic taxa in the superficial compartments evidenced the biological link with the terrestrial environment. Bacterial communities better tolerate rewetting than desiccation, since a major number of taxa tolerant to rewetting occurred in all biofilms. Overall, bacterial communities in sandy compartments showed higher resistance to flow intermittency than those in epilithic biofilms

Performance of small wind turbines: simulation of electricity supply to loads connected to the public or isolated grid

ABSTRACT The successful in the implementation of wind turbines depends on several factors, including: the wind resource at the installation site, the equipment used, project acquisition and operational costs. In this paper, the production of electricity from two small wind turbines was compared through simulation using the computer software HOMER - a national model of 6kW and an imported one of 5kW. The wind resources in three different cities were considered: Campinas (SP/BR), Cubatão (São Paulo/BR) and Roscoe (Texas/ USA). A wind power system connected to the grid and a wind isolated system - batteries were evaluated. The results showed that the energy cost ($/kWh) is strongly dependent on the windmill characteristics and local wind resource. Regarding the isolated wind system – batteries, the full supply guarantee to the simulated electrical load is only achieved with a battery bank with many units and high number of wind turbines, due to the intermittency of wind power.

Investigations on the Dynamics of Combination Maps and the Analysis of Bifurcation in a Discontinuous Logistic Map

This thesis is a study of discrete nonlinear systems represented by one dimensional mappings.As one dimensional interative maps represent Poincarre sections of higher dimensional flows,they offer a convenient means to understand the dynamical evolution of many physical systems.It highlighting the basic ideas of deterministic chaos.Qualitative and quantitative measures for the detection and characterization of chaos in nonlinear systems are discussed.Some simple mathematical models exhibiting chaos are presented.The bifurcation scenario and the possible routes to chaos are explained.It present the results of the numerical computational of the Lyapunov exponents (λ) of one dimensional maps.This thesis focuses on the results obtained by our investigations on combinations maps,scaling behaviour of the Lyapunov characteristic exponents of one dimensional maps and the nature of bifurcations in a discontinous logistic map.It gives a review of the major routes to chaos in dissipative systems,namely, Period-doubling ,Intermittency and Crises.This study gives a theoretical understanding of the route to chaos in discontinous systems.A detailed analysis of the dynamics of a discontinous logistic map is carried out, both analytically and numerically ,to understand the route it follows to chaos.The present analysis deals only with the case of the discontinuity parameter applied to the right half of the interval of mapping.A detailed analysis for the n –furcations of various periodicities can be made and a more general theory for the map with discontinuities applied at different positions can be on a similar footing

Stochastic modelling of rainfall from satellite data

Satellite-based rainfall monitoring is widely used for climatological studies because of its full global coverage but it is also of great importance for operational purposes especially in areas such as Africa where there is a lack of ground-based rainfall data. Satellite rainfall estimates have enormous potential benefits as input to hydrological and agricultural models because of their real time availability, low cost and full spatial coverage. One issue that needs to be addressed is the uncertainty on these estimates. This is particularly important in assessing the likely errors on the output from non-linear models (rainfall-runoff or crop yield) which make use of the rainfall estimates, aggregated over an area, as input. Correct assessment of the uncertainty on the rainfall is non-trivial as it must take account of • the difference in spatial support of the satellite information and independent data used for calibration • uncertainties on the independent calibration data • the non-Gaussian distribution of rainfall amount • the spatial intermittency of rainfall • the spatial correlation of the rainfall field This paper describes a method for estimating the uncertainty on satellite-based rainfall values taking account of these factors. The method involves firstly a stochastic calibration which completely describes the probability of rainfall occurrence and the pdf of rainfall amount for a given satellite value, and secondly the generation of ensemble of rainfall fields based on the stochastic calibration but with the correct spatial correlation structure within each ensemble member. This is achieved by the use of geostatistical sequential simulation. The ensemble generated in this way may be used to estimate uncertainty at larger spatial scales. A case study of daily rainfall monitoring in the Gambia, west Africa for the purpose of crop yield forecasting is presented to illustrate the method.

Assessing the uncertainty associated with intermittent rainfall fields

[1] In many practical situations where spatial rainfall estimates are needed, rainfall occurs as a spatially intermittent phenomenon. An efficient geostatistical method for rainfall estimation in the case of intermittency has previously been published and comprises the estimation of two independent components: a binary random function for modeling the intermittency and a continuous random function that models the rainfall inside the rainy areas. The final rainfall estimates are obtained as the product of the estimates of these two random functions. However the published approach does not contain a method for estimation of uncertainties. The contribution of this paper is the presentation of the indicator maximum likelihood estimator from which the local conditional distribution of the rainfall value at any location may be derived using an ensemble approach. From the conditional distribution, representations of uncertainty such as the estimation variance and confidence intervals can be obtained. An approximation to the variance can be calculated more simply by assuming rainfall intensity is independent of location within the rainy area. The methodology has been validated using simulated and real rainfall data sets. The results of these case studies show good agreement between predicted uncertainties and measured errors obtained from the validation data.