The Institute for Research in Technology (IIT) belongs to the School of Engineering (ICAI) of Comillas Pontifical University of Madrid (Comillas). Its main aim is to promote research and postgraduate training in diverse technological fields through participation in specific projects of interest for Industry and the Government. The IIT it is a non-profit Institute, which aims to be flexible and pragmatic in the way it works. It is essentially financed by projects contracted by companies, and thus responds to a clear social demand. More information in http://www.iit.comillas.edu.
The IIT has offered several PhD positions in topics that have been considered strategic as they address emerging technologies that anticipate industry needs. If you have an excellent academic background and you are interested in pursuing a doctoral degree in Power Systems or in Engineering Systems Modeling, please review the information below and apply on-line by visiting the web https://www.iit.comillas.edu/becas/index.php.en, under the field Research Assistant, Type B.
The information that you need to upload when applying is the next one:
All these offered PhD theses will be supervised by professors/researchers belonging to IIT with the possible collaboration of foreign research centers. The areas of expertise, experience and CV of the professors/researchers can be seen at https://www.iit.comillas.edu/personal/index.php.en.
For all the selected candidates we offer a 3-year full-time contract with exclusive dedication to the PhD thesis. The gross annual salary will be 17.239€/year (1.436€/month). In addition, the IIT will cover 90% of the doctorate fee, and the selected candidates will enjoy all the advantages offered to Comillas’ students.
The deadline to apply is October 1st, 2018.
1. Stability analysis of large power systems with 100% of non-synchronous generation
Future electrical energy systems all over the world are being planned to incorporate large amounts of renewable resources. Renewable resources such as wind turbines or photovoltaic (PV) solar panels are totally or partially coupled by voltage source converters (VSCs) (i.e. non-synchronous generation). Future power systems are tending to have a less-dominant presence of synchronous generators due to the incorporation of renewable generators interfaced by electronic power converters. A realistic picture of future power systems may still contain synchronous and non-synchronous generation. However, it is also reasonable to think in scenarios with 100% of non-synchronous generation, at least during a short time. For example, during some hours of the day. Transmission System Operators (TSOs) must also guarantee the stability of the system in such scenarios. Understanding the dynamics drive the stability of a large power system with 100% of non-synchronous generation remains an open question. Furthermore, the types of models to be used for stability analysis of this type of systems and how the converters should be operated are also open questions. Along these lines, the main objectives of this doctoral thesis are:
Requirements: Knowledge on power systems and dynamical systems, knowledge on Matlab/Simulink, programming skills.
2. Modelling and optimizing the behavior of distributed agents in decentralized power systems by Reinforcement Learning techniques
The decision-making process in electric power systems is a very complex task that requires decision support models. These models have evolved over time to respond to the increasing requirements of the electric power industry taking advantage of the latest advances in different areas of applied mathematics as well as the growing computing capacity.
Reinforcement Learning (RL) is an area of machine learning that studies how software agents must take their actions in an environment so as to maximize some notion of cumulative reward. In the power system of the future, both the supply and the consumption will be much more decentralized and the involved agents will be forced to make decisions continuously in a limited information environment. This decentralization can be materialized through the creation of micro-grids that are groups of distributed generation resources, consumption and storage systems that can work connected to the grid to buy/sell electric energy and system services, or in an isolated mode. The development of smart grids makes it possible to foresee that despite the existing barriers, the penetration of these micro-grids will increase considerably in the near future. The characterization of the behavior of all these distributed agents will be crucial to understand the evolution of future power systems not only due to technical reasons, but also from the economic and regulatory point of view. The traditional approach to model the behavior of rational agents through mathematical programming techniques (for example, oligopoly market equilibrium models) may not be applicable when the required simplifications give rise to unrealistic descriptions. The ability of machine learning techniques to deal with very large and complex problems makes RL a promising technique that can complement traditional mathematical programming techniques.
The main objective of this PhD thesis is to understand how the existence of distributed technologies in the form of microgrids (distributed generation, flexible demand, energy storage, and advanced power electronics and control devices) will affect our understanding of power systems.
Requirements: Creativity and autonomy; programming skills (such as Python, R, Matlab); knowledge on machine learning fundamentals; knowledge on optimization techniques; academic excellence.
3. Flexible charging of electric vehicles using Blockchain technology
The electrification of transportation is one of the key changes to achieve emissions reduction worldwide. The transport sector is the one that consumes the most energy; and it is also the one that causes the most CO2 emissions. In addition, internal combustion engines are responsible for a great deal of the pollution in urban centers. Electric vehicles (EVs) are a big part of the solution to move towards a more sustainable mobility. However, adoption of EVs faces many challenges. Electricity markets are closed to the participation of distributed resources, including EVs. European regulations, however, including the Clean Energy Package for all Europeans and Network Codes, such as the Network Code of Electricity Balancing, require creating local market mechanisms at the Distribution System Operators (DSOs) level to solve local constraints. These regulations also open the participation of distributed resources to existing markets managed by the Transmission System Operators (TSO), such as balancing markets and congestion management.
Blockchain technology is widely believed to be a key enabler to support decentralized trading, guarantee a security of transactions through smart contracts and reduce the role of intermediaries and transaction costs. All these characteristics make blockchain promising for the coordination of EVs charging. As of now blockchain technology is in its infancy, and alternative solutions are emerging and different platforms; each based on alternative consensus mechanisms that have been created to support diverse commercial applications. All these challenges mentioned before need to be addressed to effectively integrate EVs, but novel tools such as Blockchain‐like technologies can provide innovative and efficient solutions to some of those challenges.
This thesis will address these challenges to foster the adoption of EVs and simultaneously capture the maximum benefit of their flexibility for the power system. Specifically, the thesis will explore efficient solutions for EVs charging based on Blockchain-like-technology, analysing from the physical layer to the regulation layer, considering business models and technology.
Requirements: Creativity, Proactivity, Interest in Blockchain, Programing skills, Marks over 7.
4. Digital Metro
The widespread use of digital technologies in society enables new and more efficient ways to operate a few types of systems. In the case of urban mass transportation systems (metro), the digital technologies make it possible to obtain information on the users’ behavior. Therefore, making use of these data, it is theoretically possible to forecast how, when and where passengers are to get in and out of the system facilities. However, the use of forecasting techniques to improve operation efficiency or the relationship with travelers in metro systems is not commonplace yet.
In this thesis, we propose to develop, based on how travelers get in and out stations over time, a model to accurately forecast the dynamic evolution of system origin-destination matrices. With this information, a data-driven method that leads to changes in the metro system’s state derived from online measurements. In addition, we will explore new possible ways of enhancing the way to interact with and provide services to the system users.
Requirements: MSc in Industrial Engineering, MSc in Telecommunication Engineering, MSc in Big Data and Advanced Technology. Good command of advanced analytics and machine learning techniques.
5. Design of radiofrequency sensors based on metamaterials for application in bio-sanitary environments
The PhD will be focused in study, design, analysis and prototyping of radiofrequency sensors. The proposed sensors are based on printed metamaterial structures. These structures are specific geometric shapes that respond with more or less energy depending on the frequency of the sensor incident wave. These sensors can be wireless, small and low cost, which gives them ideal properties for use in the industry. Specifically, the development will be focused on the bio-sanitary environment.
Requirements: Knowledge of electronics, electronic instrumentation, microcontrollers programming, and radiofrequency circuits and antennas.Lee mas