Optimal management of smart grids using machine learning techniques

Fossil fuels -including coal, oil, and natural gas- have been powering economies for over 150 years, and currently supply about 80% of the world's energy. But this number is meant to decrease drastically before the end of the century to prevent global warming. Russia’s invasion of Ukraine has created shock waves in global energy markets, leading to price volatility, supply shortages, security issues and economic uncertainty, leading us to the biggest energy crisis of the history. To deal with these problems, our society has to take serious actions to optimize the electric supply in the world while taking into account intermittent energy sources such as solar, wind or hydraulic power. As a consequence, the target of climate neutrality by 2050 has encouraged the growth of renewable energy in Europe: in 2020, around one-fifth of the European electricity was generated from wind and solar electricity, surpassing fossil-based electricity generation. This same year, electricity generation from coal decreased by almost 50% since 2015, which is equivalent to avoiding around 320 Mt CO2 per year1. Smart grids have the potential to optimize the efficiency, reliability, economics, and sustainability of the production, distribution, and consumption of electrical energy. In 2021, the Smart Grid Index benchmarked a total of 86 Smart Grid utilities across 37 countries2, with Enedis achieving the number one position. Indeed, a classical electrical grid is defined as a reliable integrated power delivery system consisting of interconnected Distributed Energy Resources (DERs), which has the purpose of satisfying load demands without any interruption. However, introducing renewable energies in the grid requires some predictive measures in order to guarantee the reliability and stability of the energy supply as they are highly influenced by weather conditions, economic situations, and environmental issues. As a solution, Smart Grids can handle the presence of uncertainties as well as ensuring a high level of security and quality of electricity supply, minimizing the energy production and distribution costs. In this research assignment, we will try to implement a Smart Grid model using Machine Learning methods and a Model Predictive Control (MPC) as a baseline for optimal control solution to compare with Deep Learning. We will start understanding some already existent well-known cases from Matlab and try to adapt them to our Smart Grid proble

Impossible Choices: How Workers Manage Unpredictable Scheduling Practices

A total of 16 percent of hourly workers and 36 percent of workers paid on some other basis experience unstable work schedules due to irregular, on-call, rotating, or split shifts, which negatively impact workers’ ability to manage family responsibilities, finances, and health. Primarily drawing on data from in-depth interviews conducted in Oregon in 2016, this study expands research on how workers navigate through “bad jobs” by exploring the ways in which they respond in an attempt to manage the individual impacts of precarious work arrangements. We found that workers respond to unpredictable scheduling in four ways: they acquiesce, self-advocate, quit, or directly oppose employers. Our findings highlight the “impossible choices” workers face as they negotiate prevalent, unpredictable work conditions, juggle work-life obligations, and struggle to remain employed. We conclude with fair week, work policy recommendations

Parameter extraction methodology for composite right/left-handed transmission lines using quasi-static models

In this thesis, the parameter extraction process of equivalent circuit models of a composite right/left-handed (CRLH) transmission line (TL) is studied. First, an introduction to metamaterial (MTM) structures, and a brief description of the two main approaches of modeling a CRLH TL, are described. Next, it is reviewed the basic theory of modeling a purely righthanded (PRH) transmission lines by means of using ABCD-parameters. After obtaining a conventional TL model, we analize our circuital responses and compare them with the expected behavior published in the literature. Next, it is reviewed the basic theory of modeling a purely left-handed (PLH) TL by means of using ABCD-parameters. After obtaining a model of PLH TL, we analize our circuital responses and compare them with exact theoretical results published in MTM literature. Next, it is also reviewed the basic theory of modeling a CRLH TL, comparing our responses again with exact theoretical results. Next, based on previous learning of PRH TL, a parameter extraction (PE) strategy is employed on an actual microstrip structure simulated in Sonnet. Applying a minimax formulation using the Nelder-Mead method, the equivalent RGLC average values are extracted using Matlab. Later, a compilation of several CRLH TL structures are described, including its S-parameter responses. Two main groups of them are described: those using microstrip technology only, and those using surface mount technology (SMT) devices soldered over microstrip technology. Next, one of the structures is selected to finally implement a PE strategy, extracting its equivalent RGLC average values of the CRLH TL structure. It was found that the equivalent circuital model (implemented in Matlab), with parameter extracted by optimization, yelds an acceptable approximation of the CRLH TL responses obtained by fullwave elctromagnetic simulation (implemented in Sonnet) on a wide bandwidth

L'influence du tempo musical sur l'expérience de consommation d'un loisir sportif‎ : le cas de l'escalade de bloc en salle

L’influence de la musique d’ambiance sur les consommateurs est reconnue tant par les praticiens que les chercheurs. Malgré une manipulation aisée en apparence, il s’agit d’un élément atmosphérique complexe. À ce sujet, la littérature est foisonnante. Cependant, elle ne permet pas d’établir de consensus clair. Dans ce travail, les effets de la musique d’ambiance sur les consommateurs ont été observés au travers de son tempo. Pour ce faire, une quasi-expérimentation a été réalisée auprès de 148 clients d’un contexte de loisirs sportifs indoor : une salle d’escalade de bloc. Les résultats montrent qu’à cause d’un tempo trop rapide ou par sa présence, la musique d’ambiance détériore la qualité de l’expérience vécue par la clientèle : les interactions entre clients sont moins nombreuses, l’état d’immersion et la satisfaction de ces derniers, plus fragiles.The influence of background music on consumers is recognized by both practitioners and academics. Despite an apparent easy manipulation, it is a complex atmospheric element. This topic is the subject of a rich literature. However, it does not provide a clear consensus. In this work, the effects of background music on consumers were observed through its tempo. To this end, a quasi-experimentation was carried out with 148 clients in a sports leisure context. The results highlight that because of a tempo too fast or because of its presence, background music deteriorates the quality of the experience lived by customers: interactions between customers are less likely, the immersion and the satisfaction of these, more fragile

Desde la Toscana uruguaya

Nacida en la ciudad de Salto en 1932, segunda ciudad en importancia en el país luego de Montevideo, Marosa Di Giorgio desarrolló una literatura tan intensa como peculiar, tan recurrente como original, tan independiente como surrealizante (ya que no adscribe al movimiento surrealista como tal). Al punto que no es aventurado afirmar que la obra de Marosa no se parece a nadie y es inconfundiblemente propia, autónoma

L'influence du tempo musical sur l'expérience de consommation d'un loisir sportif‎ : le cas de l'escalade de bloc en salle

L’influence de la musique d’ambiance sur les consommateurs est reconnue tant par les praticiens que les chercheurs. Malgré une manipulation aisée en apparence, il s’agit d’un élément atmosphérique complexe. À ce sujet, la littérature est foisonnante. Cependant, elle ne permet pas d’établir de consensus clair. Dans ce travail, les effets de la musique d’ambiance sur les consommateurs ont été observés au travers de son tempo. Pour ce faire, une quasi-expérimentation a été réalisée auprès de 148 clients d’un contexte de loisirs sportifs indoor : une salle d’escalade de bloc. Les résultats montrent qu’à cause d’un tempo trop rapide ou par sa présence, la musique d’ambiance détériore la qualité de l’expérience vécue par la clientèle : les interactions entre clients sont moins nombreuses, l’état d’immersion et la satisfaction de ces derniers, plus fragiles.The influence of background music on consumers is recognized by both practitioners and academics. Despite an apparent easy manipulation, it is a complex atmospheric element. This topic is the subject of a rich literature. However, it does not provide a clear consensus. In this work, the effects of background music on consumers were observed through its tempo. To this end, a quasi-experimentation was carried out with 148 clients in a sports leisure context. The results highlight that because of a tempo too fast or because of its presence, background music deteriorates the quality of the experience lived by customers: interactions between customers are less likely, the immersion and the satisfaction of these, more fragile

Entrevista al Dr. Arturo Ardao

Fil: Loustaunau, Fernando