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.

Mobilizing Greater Crop and Land Potentials Sustainably

The supply side of the food security engine is the way we farm. The current engine of conventional tillage farming is faltering and needs to be replaced. This presentation will address supply side issues of agriculture to meet future agricultural demands for food and industry using the alternate no-till Conservation Agriculture (CA) paradigm (involving no-till farming with mulch soil cover and diversified cropping) that is able to raise productivity sustainably and efficiently, reduce inputs, regenerate degraded land, minimise soil erosion, and harness the flow of ecosystem services. CA is an ecosystems approach to farming capable of enhancing not only the economic and environmental performance of crop production and land management, but also promotes a mindset change for producing ‘more from less’, the key attitude towards sustainable production intensification. CA is now spreading globally in all continents at an annual rate of 10 Mha and covers some 157 Mha of cropland. Today global agriculture produces enough food to feed three times the current population of 7.21 billion. In 1976, when the world population was 4.15 billion, world food production far exceeded the amount necessary to feed that population. However, our urban and industrialised lifestyle leads to wastage of food of some 30%-40%, as well as waste of enormous amount of energy and protein while transforming crop-based food into animal-derived food; we have a higher proportion of people than ever before who are obese; we continue to degrade our ecosystems including much of our agricultural land of which some 400 Mha is reported to be abandoned due to severe soil and land degradation; and yields of staple cereals appear to have stagnated. These are signs of unsustainability at the structural level in the society, and it is at the structural level, for both supply side and demand side, that we need transformed mind sets about production, consumption and distribution. CA not only provides the possibility of increased crop yields for the low input smallholder farmer, it also provides a pro-poor rural and agricultural development model to support agricultural intensification in an affordable manner. For the high output farmer, it offers greater efficiency (productivity) and profit, resilience and stewardship. For farming anywhere, it addresses the root causes of agricultural land degradation, sub-optimal ecological crop and land potentials or yield ceilings, and poor crop phenotypic expressions or yield gaps. As national economies expand and diversify, more people become integrated into the economy and are able to access food. However, for those whose livelihoods continue to depend on agriculture to feed themselves and the rest of the world population, the challenge is for agriculture to produce the needed food and raw material for industry with minimum harm to the environment and the society, and to produce it with maximum efficiency and resilience against abiotic and biotic stresses, including those arising from climate change. There is growing empirical and scientific evidence worldwide that the future global supplies of food and agricultural raw materials can be assured sustainably at much lower environmental and economic cost by shifting away from conventional tillage-based food and agriculture systems to no-till CA-based food and agriculture systems. To achieve this goal will require effective national and global policy and institutional support (including research and education).

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.