PhD on AI-driven state estimation and prediction

PhD on AI-driven state estimation and prediction

Published Deadline Location
23 May tomorrow Eindhoven
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?

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.

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

RT2: AI-driven state estimation and prediction

This research aims to combine physics-based models, i.e. state estimation based on WLS, with physics-aware neural network structures to improve network observability and grid monitoring capability. Besides a normal estimation of system state, it should be also possible to determine anomalous events using AI-driven techniques. More specifically, the research will include:
  • Development of semi real-time measurement solutions
  • Uncertainty modeling of (synthetic) LV load/generation profiles
  • Increasing network observability with physics-aware neural network algorithms
  • Anomaly detection and mitigation solutions against anomalous attacks
  • 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.
PhD3 / RT3: Stochastic modelling and reliability assessment.
PhD4 / RT4: Grid-edge optimal solutions.

Specifications

Eindhoven University of Technology (TU/e)

Requirements

  • A MSc degree related to modeling and analysis of power systems and distributed energy resources.
  • Having a good understanding of distribution grid planning and operation.
  • Experience in data-driven modeling, probabilities, stochastic optimization solutions is an advantage.
  • Skills in scientific programming and/or numerical computing in languages like Python, Julia, or MATLAB are advantages.
  • Excellent modeling skills in simulation tools such as PowerFactory or other equivalent open-source power system packages. Having experience in connecting such simulation tools with the programming environment of MATLAB and/or Python is an advantage.
  • Enthusiasm in open-source and motivated to learn basic skills of scientific software engineering.
  • Ability to work in an interdisciplinary team and interested in collaborating with industrial partners.
  • Motivated to develop your teaching skills and coach MSc and BSc students.
  • Fluent in spoken and written English (C1 level).
  • Dutch language skill is an advantage.

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.
  • Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates. 

Additional information

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, Phuong Nguyen, associate professor, p.nguyen.hong@tue.nl or +31 40 247 5830.

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.

Specifications

  • PhD
  • Engineering
  • max. 38 hours per week
  • University graduate
  • V36.7491

Employer

Eindhoven University of Technology (TU/e)

Learn more about this employer

Location

De Rondom 70, 5612 AP, Eindhoven

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Application procedure

Application procedure

Make sure to apply no later than 23 Jun 2024 23:59 (Europe/Amsterdam).