E-2340 Phd in Data Driven Approaches for Real Time

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EschsurAlzette, Luxembourg Luxembourg Institute of Science and Technology (LIST) Temps plein

Description
**Are you passionate about research? So are we Come and join us**:
The Luxembourg Institute of Science and Technology (LIST) is a Research and Technology Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities in their decisions and businesses in their strategies.

**You‘d like to contribute as research support contributor? Join our Environmental Research and Innovation department**:
The Environmental Research and Innovation (ERIN) department, made up of 200 life science, environmental science and information technology researchers and engineers, provides the interdisciplinary knowledge, expertise and technologies to lead solutions including the major environmental challenges facing society, such as climate change mitigation, ecosystem resilience, sustainable energy systems, efficient use of renewable resources, and environmental pollution prevention and control.

As a PhD student, you will join the recently established Intelligent and Clean Energy Systems (ICES) research unit and will in particular be involved in its hardware-in-the-loop (HIL) simulation laboratory. The ICES research unit aims to develop ground-breaking market-oriented solutions in the ways in which energy is generated, distributed and consumed, meeting the challenge of cutting greenhouse-gas emissions, ensuring energy security, planning feasible business models and socially outreach advances.

Essential to the ICES unit is the smart grid vision, namely, an electricity network in which information flows freely between consumers and suppliers, and demand adapts in real-time in response to the continuously changing supply from intermittent renewable sources. The ICES unit however extends this vision toward a wider energy portfolio, integrating other energy infrastructures together to electricity, and providing further abstraction and intelligence levels in the conception of solutions for both planning and operating the energy systems of the future. Moreover, in addition to generate impacting solutions addressing society and market needs, this research unit will conduct fundamental research aiming ground-breaking disclosers to remove barriers and introduce revolutionary concepts for planning, developing and operating the Intelligent Clean Energy Systems of the future.

**Responsibilities**:
**How will you contribute?**:
The unprecedented energy transition driven by the need to achieve carbon neutrality is changing the energy industry landscape, especially with the large-scale penetration of renewable energy and distributed energy resources (e.g., electric vehicles, battery storage) at local distribution level. There is a pressing need to make decisions in situ and moving to real-time (RT) according to the instantaneous operating conditions of the distributed girds. Operational planning will evolve towards optimal control of complex systems in RT, which cannot be addressed by existing approaches. The challenge is twofold:

- Future distributed grids with DERs will face serious barriers due to increasing complexity, mainly non-linearity and uncertainty that need to be tackled in RT operation. This will lead to an unsolved problem for conventional operation methods, which are mostly based on physical model analysis.
- The vast number of energy assets (e.g., DERs) in future distribution grids will give rise to intractable operational problems by using conventional techniques based on a centralized approach. This will hinder the penetration of renewable sources and the integration of end-users in the system operation. As a result, it is required to rethink how to perform distribution grid optimization considering the security of the systems to invent new, accurate, and computationally efficient framework.

For this PhD studentship, you will work in the frame of LEAP project (funded by National Research Fund of Luxembourg), which proposes an intelligent data-driven solution for optimizing and coordinating energy assets in future distributed grids at multiple spatiotemporal scales, i.e., new forms of Artificial Intelligence (AI) based operation and analysis tools that can leverage the RT big datasets associate with these systems. LEAP will integrate into National Wide Digital Twin (NWDT) project at LIST and provide a set of cloud-edge based tools for enabling optimizing and coordinating the operation of distribution grids with DERs in RT under generic operating conditions.

Furthermore, you will address the research question: What autonomous tools based on AI, utilizing the power of big energy datasets, should be developed to effectively enable the RT operation of future distribution grids with DERs? Accordingly, the main objective is to identify an intelligent data-centric solution for optimizing and coordinating energy assets in future distribution grids at multiple spatiotempora

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