A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

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.

Load Profile Analysis Tool for Electrical Appliances in Households

This paper presents a methodology to forecast the hourly and daily consumption in households. The methodology was validated for households in Lisbon region, Portugal. The paper shows that the forecast tool allows obtaining satisfactory results for forecasting. Models of demand response allow the support of consumer’s decision in exchange for an economic benefit by the redefinition of load profile or changing the appliance consumption period. It is also in the interest of electric utilities to take advantage of these changes, particularly when consumers have an action on the demand-side management or production. Producers need to understand the load profile of households that are connected to a smart grid, to promote a better use of energy, as well as optimize the use of micro-generation from renewable sources, not only to delivering to the network but also in self-consumption.

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.