Computational Models Development and Demand Response Application for Smart Grids

This paper focuses on computational models development and its applications on demand response, within smart grid scope. A prosumer model is presented and the corresponding economic dispatch problem solution is analyzed. The prosumer solar radiation production and energy consumption are forecasted by artificial neural networks. The existing demand response models are studied and a computational tool based on fuzzy clustering algorithm is developed and the results discussed. Consumer energy management applications within the InovGrid pilot project are presented. Computation systems are developed for the acquisition, monitoring, control and supervision of consumption data provided by smart meters, allowing the incorporation of consumer actions on their electrical energy management. An energy management system with integration of smart meters for energy consumers in a smart grid is developed.

Redes elétricas inteligentes: desenvolvimento de modelos computacionais e aplicações para a gestão do lado da procura

Esta tese incide sobre o desenvolvimento de modelos computacionais e de aplicações para a gestão do lado da procura, no âmbito das redes elétricas inteligentes. É estudado o desempenho dos intervenientes da rede elétrica inteligente, sendo apresentado um modelo do produtor-consumidor doméstico. O problema de despacho económico considerando previsão de produção e consumo de energia obtidos a partir de redes neuronais artificiais é apresentado. São estudados os modelos existentes no âmbito dos programas de resposta à procura e é desenvolvida uma ferramenta computacional baseada no algoritmo de fuzzy-clustering subtrativo. São analisados perfis de consumo e modos de operação, incluindo uma breve análise da introdução do veículo elétrico e de contingências na rede de energia elétrica. São apresentadas aplicações para a gestão de energia dos consumidores no âmbito do projeto piloto InovGrid. São desenvolvidos sistemas de automação para, aquisição monitorização, controlo e supervisão do consumo a partir de dados fornecidos pelos contadores inteligente que permitem a incorporação das ações dos consumidores na gestão do consumo de energia elétrica; SMART GRIDS - COMPUTATIONAL MODELS DEVELOPMENT AND DEMAND SIDE MANAGMENT APPLICATIONS Abstract: This thesis focuses on the development of computational models and its applications on the demand side management within the smart grid scope. The performance of the electrical network players is studied and a domestic prosumer model is presented. The economic dispatch problem considering the production forecast and the energy consumption obtained from artificial neural networks is also presented. The existing demand response models are studied and a computational tool based on the fuzzy subtractive clustering algorithm is developed. Energy consumption profiles and operational modes are analyzed, including a brief analysis of the electrical vehicle and contingencies on the electrical network. Consumer energy management applications within the scope of InovGrid pilot project are presented. Computational systems are developed for the acquisition, monitoring, control and supervision of consumption data provided by smart meters allowing to incorporate consumer actions on their electrical energy management.

“A long-term mortality analysis of subsidized firms in rural areas: an empirical study in the Portuguese Alentejo region”

Studies have demonstrated that public policies to support private firms’ investment have the ability to promote entrepreneurship, but the sustainability of subsidized firms has not often been analysed. This paper aims to examine this dimension specifically through evaluating the mortality of subsidized firms in the long-term. The analysis focuses on a case study of the LEADER+ Programme in the Alentejo region of Portugal. With this purpose, the paper examines the activity status (active or not active) of 154 private, rural, for-profit firms in Alentejo that had received a subsidy to support investment between 2002 and 2008 under the LEADER+ Programme. The methodology is based on binary choice models in order to study the probability of these firms still being active. The explanatory variables used are the following: (1) the characteristics of entrepreneurs and managers’ strategic decisions, (2) firm profile and characteristics, (3) regional economic environment. Data assessment showed that the cumulative mortality rate of firms on 31st December 2013 is over 20 %. Interpretation of the regression model revealed that he probability of firms’ survival increases with higher investment, firm age and regional business concentration, whereas the number of applications made by firms has a negative impact on their survival. So it seems that for subsidized firms the amount of investment is as important as its frequency.

New measures for assessing model equilibrium and prediction mismatch in species distribution models

Models based on species distributions are widely used and serve important purposes in ecology, biogeography and conservation. Their continuous predictions of environmental suitability are commonly converted into a binary classification of predicted (or potential) presences and absences, whose accuracy is then evaluated through a number of measures that have been the subject of recent reviews. We propose four additional measures that analyse observation-prediction mismatch from a different angle – namely, from the perspective of the predicted rather than the observed area – and add to the existing toolset of model evaluation methods. We explain how these measures can complete the view provided by the existing measures, allowing further insights into distribution model predictions. We also describe how they can be particularly useful when using models to forecast the spread of diseases or of invasive species and to predict modifications in species’ distributions under climate and land-use change

Modelling and studies of the spectral response of some optoelectronic components

Solar radiation takes in today's world, an increasing importance. Different devices are used to carry out spectral and integrated measurements of solar radiation. Thus the sensors can be divided into the fallow types: Calorimetric, Thermomechanical, Thermoelectric and Photoelectric. The first three categories are based on components converting the radiation to temperature (or heat) and then into electrical quantity. On the other hand, the photoelectric sensors are based on semiconductor or optoelectronic elements that when irradiated change their impedance or generate a measurable electric signal. The response function of the sensor element depends not only on the intensity of the radiation but also on its wavelengths. The radiation sensors most widely used fit in the first categories, but thanks to the reduction in manufacturing costs and to the increased integration of electronic systems, the use of the photoelectric-type sensors became more interesting. In this work we present a study of the behavior of different optoelectronic sensor elements. It is intended to verify the static response of the elements to the incident radiation. We study the optoelectronic elements using mathematical models that best fit their response as a function of wavelength. As an input to the model, the solar radiation values are generated with a radiative transfer model. We present a modeling of the spectral response sensors of other types in order to compare the behavior of optoelectronic elements with other sensors currently in use.