4 Phds On Ai-driven Applications for Smart
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**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.
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.
The four main research tracks (RTs) of AISO are as follows:
**RT1: Synthetical data generation using multivariate models**
This research will focus on a scalable approach by using multivariate models to capture uncertainties in spatial-temporal correlations. It is expected to combine both top-down and bottom-up approaches into a scalable and stable solution. More specifically, the research will include:
- Requirement specifications for both real-time state estimation and long-term power flow analysis
- Design algorithms which combine the aggregated data and anonymized individual data
- Validation of the developed algorithms in the simulation environment with grid use-case scenarios
- Integrate and validate solutions in the virtual grid environment.
**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.
**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
- Developmen
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