PhD on AI-driven state estimation and prediction

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.

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.

Salary Benefits:

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. 

38 hours per week

De Rondom 70