Optimization of Aggregators Energy Resources considering Local Markets and Electric Vehicle Penetration

O sector elétrico tem vindo a evoluir ao longo do tempo. Esta situação deve-se ao facto de surgirem novas metodologias para lidarem com a elevada penetração dos recursos energéticos distribuídos (RED), principalmente veículos elétricos (VEs). Neste caso, a gestão dos recursos energéticos tornou-se mais proeminente devido aos avanços tecnológicos que estão a ocorrer, principalmente no contexto das redes inteligentes. Este facto torna-se importante, devido à incerteza decorrente deste tipo de recursos. Para resolver problemas que envolvem variabilidade, os métodos baseados na inteligência computacional estão a se tornar os mais adequados devido à sua fácil implementação e baixo esforço computacional, mais precisamente para o caso tratado na tese, algoritmos de computação evolucionária (CE). Este tipo de algoritmo tenta imitar o comportamento observado na natureza. Ao contrário dos métodos determinísticos, a CEé tolerante à incerteza; ou seja, é adequado para resolver problemas relacionados com os sistemas energéticos. Estes sistemas são geralmente de grandes dimensões, com um número crescente de variáveis e restrições. Aqui a IC permite obter uma solução quase ótima em tempo computacional aceitável com baixos requisitos de memória. O principal objetivo deste trabalho foi propor um modelo para a programação dos recursos energéticos dos recursos dedicados para o contexto intradiário, para a hora seguinte, partindo inicialmente da programação feita para o dia seguinte, ou seja, 24 horas para o dia seguinte. Esta programação é feita por cada agregador (no total cinco) através de meta-heurísticas, com o objetivo de minimizar os custos ou maximizar os lucros. Estes agregadores estão inseridos numa cidade inteligente com uma rede de distribuição de 13 barramentos com elevada penetração de RED, principalmente energia renovável e VEs (2000 VEs são considerados nas simulações). Para modelar a incerteza associada ao RED e aos preços de mercado, vários cenários são gerados através da simulação de Monte Carlo usando as funções de distribuição de probabilidade de erros de previsão, neste caso a função de distribuição normal para o dia seguinte. No que toca à incerteza no modelo para a hora seguinte, múltiplos cenários são gerados a partir do cenário com maior probabilidade do dia seguinte. Neste trabalho, os mercados locais de eletricidade são também utilizados como estratégia para satisfazer a equação do balanço energético onde os agregadores vão para vender o excesso de energia ou comprar para satisfazer o consumo. Múltiplas metaheurísticas de última geração são usadas para fazer este escalonamento, nomeadamente Differential Evolution (DE), Hybrid-Adaptive DE with Decay function (HyDE-DF), DE with Estimation of Distribution Algorithm (DEEDA), Cellular Univariate Marginal Distribution Algorithm with Normal-Cauchy Distribution (CUMDANCauchy++), Hill Climbing to Ring Cellular Encode-Decode UMDA (HC2RCEDUMDA). Os resultados mostram que o modelo proposto é eficaz para os múltiplos agregadores com variações de custo na sua maioria abaixo dos 5% em relação ao dia seguinte, exceto para o agregador e de VEs. É também aplicado um teste Wilcoxon para comparar o desempenho do algoritmo CUMDANCauchy++ com as restantes meta-heurísticas. O CUMDANCauchy++ mostra resultados competitivos tendo melhor performance que todos os algoritmos para todos os agregadores exceto o DEEDA que apresenta resultados semelhantes. Uma estratégia de aversão ao risco é implementada para um agregador no contexto do dia seguinte para se obter uma solução mais segura e robusta. Os resultados mostram um aumento de quase 4% no investimento, mas uma redução de até 14% para o custo dos piores cenários.The electrical sector has been evolving. This situation is because new methodologies emerge to deal with the high penetration of distributed energy resources (DER), mainly electric vehicles (EVs). In this case, energy resource management has become increasingly prominent due to the technological advances that are taking place, mainly in the context of smart grids. This factor becomes essential due to the uncertainty of this type of resource. To solve problems involving variability, methods based on computational intelligence (CI) are becoming the most suitable because of their easy implementation and low computational effort, more precisely for the case treated in this thesis, evolutionary computation (EC) algorithms. This type of algorithm tries to mimic behavior observed in nature. Unlike deterministic methods, the EC is tolerant of uncertainty, and thus it is suitable for solving problems related to energy systems. These systems are usually of high dimensions, with an increased number of variables and restrictions. Here the CI allows obtaining a near-optimal solution in good computational time with low memory requirements. This work's main objective is to propose a model for the energy resource scheduling of the dedicated resources for the intraday context, for the our-ahead, starting initially from the scheduling done for the day ahead, that is, 24 hours for the next day. This scheduling is done by each aggregator (in total five) through metaheuristics to minimize the costs or maximize the profits. These aggregators are inserted in a smart city with a distribution network of 13 buses with a high penetration of DER, mainly renewable energy and EVs (2000 EVs are considered in the simulations). Several scenarios are generated through Monte Carlo Simulation using the forecast errors' probability distribution functions, the normal distribution function for the day-ahead to model the uncertainty associated with DER and market prices. Multiple scenarios are developed through the highest probability scenario from the day-ahead when it comes to intraday uncertainty. In this work, local electricity markets are used as a mechanism to satisfy the energy balance equation where each aggregator can sell the excess of energy or buy more to meet the demand. Several recent and modern metaheuristics are used to solve the proposed problems in the thesis, namely Differential Evolution (DE), Hybrid-Adaptive DE with Decay function (HyDE-DF), DE with Estimation of Distribution Algorithm (DEEDA), Cellular Univariate Marginal Distribution Algorithm with NormalCauchy Distribution (CUMDANCauchy++), Hill Climbing to Ring Cellular Encode-Decode UMDA (HC2RCEDUMDA). Results show that the proposed model is effective for the multiple aggregators. The metaheuristics present satisfactory results and mostly less than 5% variation in costs from the day-ahead except for the EV aggregator. A Wilcoxon test is also applied to compare the performance of the CUMDANCauchy++ algorithm with the remaining metaheuristics. CUMDANCauchy++ shows competitive results beating all algorithms in all aggregators except for DEEDA, which presents similar results. A risk aversion strategy is implemented for an aggregator in the day-ahead context to get a safer and more robust solution. Results show an increase of nearly 4% in day-ahead cost but a reduction of up to 14% of worst scenario cost

Análisis y diseño de una Infraestructura convergente. Caso de estudio Vblock

La Infraestructura Convergente busca solventar las necesidades de un mercado que es cada vez más exigente y que se ve insatisfecho por la falta de capacidad de respuesta de un equipo de TI que debe destinar la mayor parte de su tiempo a integrar y dar mantenimiento a la infraestructura muy diversa con la que cuentan y que dificulta el aprovisionamiento de la misma. Este sistema se enmarca en una infraestructura homogénea con componentes de cómputo, red y almacenamiento que se encuentren integrados desde fábrica permitiendo tener un solo punto de gestión, mantenimiento y aprovisionamiento. VBlock es una Infraestructura Convergente, creada por VCE y construida por elementos Cisco. La implementación de este sistema permitirá al departamento de TI ofrecer una respuesta más eficiente al usuario, optimizando tiempos de respuesta así como costos de ejecución

Hippocampal Insulin Signaling And Neuroprotection Mediated By Physical Exercise In Alzheimer´s Disease

Epidemiological studies indicate continuous increases in the prevalence of Alzheimer’s Disease (AD) in the next few decades. The key feature of this disease is hippocampal neurodegeneration. This structure has an important role in learning and memory. Intense research efforts have sought to elucidate neuroprotective mechanisms responsible for hippocampal integrity. Insulin signaling seems to be a very promising pathway for the prevention and treatment of AD. This hormone has been described as a powerful activator of neuronal survival. Recent research showed that reduced insulin sensitivity leads to low-grade inflammation, and both phenomena are closely related to AD genesis. Concomitantly, exercise has been shown to exert anti-inflammatory effects and to promote improvement in insulin signaling in the hippocampus, which supports neuronal survival and constitutes an interesting non-pharmacological alternative for the prevention and treatment of AD. This review examines recent advances in understanding the molecular mechanisms involved in hippocampal neuroprotection mediated by exercise23especia

Evolução sanitária da zona controlada da Associação dos Apicultores da Beira Alta

A apicultura é um setor em mudança, seja pelas alterações climáticas ou pela entrada de novas doenças e predadores. Assim, urge fazer um balanço da evolução legislativa e sanitária da apicultura portuguesa e cruzá-la com a realidade da Zona Controlada da Beira Alta. Em Portugal começou-se a falar em Zonas Controladas, corria o ano de 2000, ainda que o primeiro documento que surgiu para a definição deste novo conceito tenha sido publicado em 2001 (Oliveira, 2001). As Zonas Controladas sofreram um grande impulso, por força legislativa e financeira, mas atualmente, tem sido notório o desinteresse dos apicultores e Entidades Gestoras das Zonas Controladas. A 16 de Janeiro de 2024, fará 10 anos que foi homologada a Zona Controlada da Beira Alta, pelo que será importante fazer um balaço da evolução do número de apicultores e colónias ao longo destes anos, mas sobretudo uma comparação efetiva da evolução sanitária. Com a implementação do Plano Sanitário para a Zona Controlada, é de esperar que exista um maior controlo das doenças e uma consequente redução da sua prevalência.Beekeeping is a sector in flux, whether due to climate change or the entry of new diseases and predators. Therefore, it is urgent to take stock of the legislative and health evolution of Portuguese beekeeping and compare it with the reality of the Beira Alta Controlled Zone. In Portugal, people started talking about Controlled Zones in the year 2000, although the first document that emerged to define this new concept was published in 2001 (Oliveira, 2001). The Controlled Zones have received a major boost, due to legislative and financial force, but currently, the lack of interest on the part of beekeepers and Management Entities of the Controlled Zones has been notable. On January 16, 2024, it will be 10 years since the Beira Alta Controlled Zone was approved, so it will be important to take stock of the evolution of the number of beekeepers and colonies over these years, but above all an effective comparison of health developments. With the implementation of the Health Plan for the Controlled Zone, it is expected that there will be greater control of diseases and a consequent reduction in their prevalence

Enhancing Decision-making Systems with Relevant Patient Information by Leveraging Clinical Notes

[Abstract] Hospitalised patients suffering from secondary illnesses that require daily medication typically need personalised treatment. Although clinical guidelines were designed considering those circumstances, existing decision-support features fail in assimilating detailed relevant patient information, which opens up opportunities for systems capable of performing a real-time evaluation of such data against existing knowledge and providing recommendations during clinical treatments. In this paper, we present a proposal for a new feature to integrate with electronic health record (EHR) systems that enriches the health treatment process by automatically extracting information from patient medical notes and aggregating it in clinical protocols. Our goal is to leverage the historical component of the patient trajectory to improve clinical decision support systems performance.EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking; 806968NETDIAMOND project; POCI-01-0145-FEDER-016385Foundation for Science and Technology; PD/BD/142878/2018Foundation for Science and Technology; SFRH/BD/147837/201

Discovery of biomedical databases through semantic questioning

Many clinical studies are greatly dependent on an efficient identification of relevant datasets. This selection can be performed in existing health data catalogues, by searching for available metadata. The search process can be optimised through questioning-answering interfaces, to help researchers explore the available data present. However, when searching the distinct catalogues the lack of metadata harmonisation imposes a few bottlenecks. This paper presents a methodology to allow semantic search over several biomedical database catalogues, by extracting the information using a shared domain knowledge. The resulting pipeline allows the converted data to be published as FAIR endpoints, and it provides an end-user interface that accepts natural language questions.This work has received support from the EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. AP and JRA are funded by the FCT - Foundation for Science and Technology (national funds) under the grants PD/BD/142877/2018 and SFRH/BD/147837/2019 respectively.info:eu-repo/semantics/publishedVersio

A Recommender System to Help Refining Clinical Research Studies

[Abstract] The process of refining the research question in a medical study depends greatly on the current background of the investigated subject. The information found in prior works can directly impact several stages of the study, namely the cohort definition stage. Besides previous published methods, researchers could also leverage on other materials, such as the output of cohort selection tools, to enrich and to accelerate their own work. However, this kind of information is not always captured by search engines. In this paper, we present a methodology, based on a combination of content-based retrieval and text annotation techniques, to identify relevant scientific publications related to a research question and to the selected data sources.This work has received support from the EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. JFS and JRA are funded by the FCT - Foundation for Science and Technology (national funds) under the grants PD/BD/142878/2018 and SFRH/BD/147837/2019 respectively.Portugal. Fundação para a Ciência e a Tecnologia; PD/BD/142878/2018Portugal. Fundação para a Ciência e a Tecnologia; SFRH/BD/147837/201

A Recommender System Based on Cohorts’ Similarity

[Abstract] Aiming to better understand the genetic and environmental associations of Alzheimer's disease, many clinical trials and scientific studies have been conducted. However, these studies are often based on a small number of participants. To address this limitation, there is an increasing demand of multi-cohorts studies, which can provide higher statistical power and clinical evidence. However, this data integration implies dealing with the diversity of cohorts structures and the wide variability of concepts. Moreover, discovering similar cohorts to extend a running study is typically a demanding task. In this paper, we present a recommendation system to allow finding similar cohorts based on profile interests. The method uses collaborative filtering mixed with context-based retrieval techniques to find relevant cohorts on scientific literature about Alzheimer's diseases. The method was validated in a set of 62 cohorts.National Science Foundation (Portugal); POCI-01-0145-FEDER-01638

Ventilation and blood lactate in children during a maximal incremental cycling test

Este estudio analiza la respuesta ventilatoria en 46 niños varones (8,28 ± 1 años) durante una prueba máxima incremental en cicloergómetro y las concentraciones de lactato sanguíneo al final de la prueba. El incremento fue de 10 vatios cada minuto y se inició a 25 vatios. Durante la prueba el aire espirado se recolectó a través de una mascarilla facial y se analizó respiración a respiración. El segundo umbral ventilatorio (VT 2 ) se determinó según los métodos de intercambio de gases. Todos los niños alcanzaron una potencia máxima (P máx ) de 82,4 ± 1,6 W y un consumo pico de oxígeno (VO 2 ) de 44,69 ± 3,01 ml/kg/min. El VT 2 estaba en el 86,5% del VO 2pico . El lactato sanguíneo al final de la prueba fue de 9,65 ± 1,58 mM/l. Las concentraciones de lactato sanguíneo son mucho mayores que las registradas en la mayoría de los estudios previos y no parecen ser diferentes a las observadas en deportistas bien entrenados al final de una prueba similar. La ecuación que obtuvimos de la relación entre producción de dióxido de carbono (VCO 2 ) y ventilación (VE) fue lineal (y = 0,0324x - 0,008; R 2 = 0,999). En comparación con adultos evaluados previamente en nuestro laboratorio (y = 0,0347x + 0,1452; R 2 = 0,9854) fueron prácticamente idénticas. Esto puede ser un argumento válido para considerar que la capacidad de eliminar CO 2 en niños es tan alta como la de los adultos.This study analyzes the ventilatory response in 46 male children (8.28 ± 1 year) during a maximal incremental test in cycle ergometer and the blood lactate concentrations at the end of the test. The increase was 10 watts every minute starting at 25 watts. During the test the expired air was collected through a facial mask and analyzed breath by breath. The second ventilatory threshold (VT 2 ) was determined according to gas exchange methods. All children carried out a maximum power (P max ) of 82.4 ± 1.6 W and a peak oxygen consumption (VO 2 ) of 44.69 ± 3.01 mL/kg/min. The VT 2 was at 86.5% of peak VO 2 . The blood lactate at the end of the test was 9.65 ± 1.58 mM/L. The blood lactate concentrations are much higher than those reported in most studies and they do not seem to be different to those observed in well-trained sportsmen at the end of a similar test. We obtained the equation for the relationship between carbon dioxide production (VCO 2 ) and ventilation (VE) and it was linear (y = 0.0324x - 0.008; R2 = 0.999). When compared with adults previously assessed in our laboratory (y = 0.0347x + 0.1452; R 2 = 0.9854) they were practically identical. This may be a good argument to consider the ability for eliminating carbon dioxide in children as high as that in ad

Methodology to identify a gene expression signature by merging microarray datasets

A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.info:eu-repo/semantics/publishedVersio