PhD on Stochastic modelling and reliability assessment

Disruptive innovations are needed in managing and operating distribution grids. Are you our next PhD researchers in exploring disruptive innovations in managing and operating distribution grids?

Position PhD-student

Irène Curie Fellowship No

Department(s) Mathematics and Computer Science

Institutes and others EIRES - Eindhoven Institute for Renewable Energy Systems, EAISI - Eindhoven Artificial Intelligence Systems Institute

FTE 1,0

Date off 29/09/2024

Reference number V32.7492

Job description

The role of the distribution system operator (DSO) is changing from a passive maintainer of electricity networks to an active coordinator in the edge of the energy system. At the same time, customers become enabled to change from passive energy users to active participants in the local electricity system. Maintaining privacy and grid (cyber) security levels are part of the challenge to face.

A digital transformation at the edge of the distribution grid and at connected customers is unfolding. This opens possibilities for deploying distributed intelligence to enable smart network operations by collecting and processing data while preserving high levels of privacy for the customers. Exploring AI models for smart System Operation (AISO) is a collaboration project in which DSO Alliander will work together with the TU/e departments of Electrical Engineering (Electrical Energy Systems group) and Mathematics & Computer Science (Interconnected Resource-aware Intelligent Systems, and Stochastic Operations Research) to realize these innovations.

The project will be part of the TU/e’s Eindhoven AI Systems Institute (EAISI) and Eindhoven Institute for Renewable Energy Systems (EIRES) programs and therefore share, learn, and disseminate within the EAISI and EIRES communities and through the TU/e master programs Data Science and AI, Medical Engineering and AI Engineering Systems, and educational activities from the TU/e Electrical Energy Systems group and Math & Computer Science department.

If you are eager to work with a multi-disciplinary team focusing on AI-driven applications to support the DSO then this is the right position for you.   

Job Description

The project focuses on synthetical data generation, AI-driven state estimation, stochastic modelling and reliability assessment, and grid-edge optimal solutions. These models will be combined with the AI-driven state estimations to enhance network observability and grid monitoring. Additionally, integration with the stochastic modelling and reliability assessment process will provide valuable insights into the impact of uncertainties on grid reliability. Finally, in conjunction with the developed edge intelligence, these advancements will enable optimal solutions for the electricity grids in the Netherlands and e.g. the rest of Europe, while maintaining user privacy.

The research results will be immediately utilized by Alliander for congestion estimation and flexibility procurement. To achieve this, it is part of this project that all the developed (AI-driven) models and algorithms are also implemented in production-ready open-source packages.

One of the four main research tracks (RTs) of AISO is as follows:

RT3: Stochastic modelling and reliability assessment

This research will perform a microscopic bottom-up approach to develop a thorough understanding of how various component affect overall network reliability. To this end, we will develop detailed agent-based probabilistic models to examine various vulnerability assessments, like the event of unacceptable voltage fluctuations, or degradation acceleration of temporary exceeding thermal limits, relevant to the daily operations of power systems. More specifically, the research will include:

• Development of multivariate uncertainty models describing random user behavior (e.g. arrival patterns of electric vehicles and user preferences (either behavioral or utility-based)
• Development of physical models of distribution grids, in particular for voltages: tractable linearized distflow models or less tractable but more realistic models
• Integration of component degradation models along with the grid models
• Development of stochastic models considering the uncertainties in load and weather forecasting
• Integrate and validate solutions in the virtual grid environment.

See for the other 3 reseach tracks below:

PhD1 / RT1: Synthetical data generation using multivariate models.
PhD2 / RT2: AI-driven state estimation and prediction.
PhD4 / RT4: Grid-edge optimal solutions.

Job requirements

• A MSc degree in Computer Science, Data Science, or related fields
• A strong background in deep learning, distributed ML, and AI model optimization
• Good scientific programming skills and experience in languages such as Python, C++, Julia, etc.
• Enthusiasm in open-source and motivated to learn basic skills of scientific software engineering.
• Strong analytical, implementation, and experimentation skills
• Ability to work in an interdisciplinary team and be a team player
• Motivated to develop your teaching skills and coach MSc and BSc students
• Fluent in spoken and written English (C1 level)

Conditions of employment

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates. 

Information and application

About us

Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow. 

Curious to hear more about what it’s like as a PhD at TU/e? You can navigate here.

Information 

Do you recognize yourself in this profile and would you like to know more?
Please contact the hiring manager, Bert Zwart, professor, a.p.zwart@tue.nl or +31 40 247 7391.

Visit our website for more information about the application process or the conditions of employment. You can also contact HRServices@tue.nl.
Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application

We invite you to submit a complete application by using the apply button.
The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.