| Mots-clés | Digitalisation de l'énergie, data science, flexibilité, smart grids, prévision, optimisation Energy digitalization, data science, flexibility, smart grids, forecasting, optimization |
| Résumé | Context and challenges:
In the context of the energy transition, power grids integrate massive amounts of renewable generation (mostly wind and solar) whose volatility and uncertainty bring unprecedented challenges to the grid operation. Flexible generation and demand, as well as storage or storage-like resources, are key for the efficient and reliable management of future power systems. The quest for flexibility is paramount at different temporal but also spatial scales (at the transmission level, at the perimeter of an aggregator or, more locally, at distribution networks) and has expanded to multi-energy systems, e.g., the coupling between electrical and gas networks. Existing methodologies that propose flexibility indicators at a national level need to be revisited at the local level, by considering local characteristics and uncertainties in production and demand at a given territory (district, region), and accounting for events that deviate from normal operating conditions (e.g., peaks due to electric vehicle charging, low renewable availability during long periods, etc.). In a given territory, flexibility valorization raises a multitude of territory-specific questions, e.g.: Should a local flexibility market be deployed? What is the potential of local energy communities? How do local conditions affect territory-level decisions for the flexibility provision and use?
Main objective of the thesis:
The overarching objective of this research project is to develop an approach for the optimal provision and use of flexibility at the level of a territory, which accounts for the uncertainties associated with local renewable production and local energy consumption of the potential flexible consumers (residential, commercial, industrial).
Methodology and expected results:
The first step of this research project is to define flexibility provision indicators, based on production/consumption adequacy and contextual assessment at the level of a territory, relying on predictive methodologies to quantify the local flexibility potential. These indicators will be used as inputs to produce a risk-aware analytical decision-aid methodology of flexibility valorization in multi-energy systems (second step), employing forecasting models and optimization. The third step is to simplify the arguably complex modelling chain by integrating forecasting and optimization via end-to-end learning of flexibility decisions based on AI, thus predicting directly flexibility decisions that are optimal as a function of the predicted local weather conditions (e.g. uncertain mixed-cloudy day) and the local socio-economic context (e.g. high commercial activity expected due to fair/sales etc.). ... |
| Profil candidat | Profile:
Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained).
Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Skills in programming (eg R, Python, Julia,…). A succesful candidate will have a solid background in three or more of the following competencies:
• applied mathematics, statistics and probabilities
• data science, artificial intelligence
• optimisation
• energy forecasting
• power system management, integration of renewables
Expected level in french : bon niveau souhaitable
Expected level in english : excellent
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Please send the following elements by email (in pdf form) to Prof. George Kariniotakis (georges.kariniotakis@minesparis.psl.eu) AND to Associate Prof. Panagiotis Andrianesis (panagiotis.andrianesis@minesparis.psl.eu):
• Curriculum vitae (CV).
• Motivational letter for the application (cover letter).
• Contact details of two individuals that can provide a letter of reference (and eventually available already letters of reference).
• Copy of grade transcripts and last diploma (in English or French).
Please use in the title of email the acronym of this PhD topic 'PHD-2024-ERSEI-FlexEnergy'
Deadline for applications: 29/02/2024. The position will stay open until a suitable candidate is found.
Desired starting date is 1st of April 2024 or on a mutually agreed date until the 1st of September 2024. Duration 36 months.
Do not hesitate to email to the above addresses for an early expression of interest and for further information on the position. ... |
