Phd#2 At Mines Paris In Data Science
Há 5 dias
Organisation/Company: Mines Paris - PSL, Centre PERSEE
Research Field: Engineering Technology » Energy technology
Researcher Profile: Recognised Researcher (R2), Leading Researcher (R4), First Stage Researcher (R1), Established Researcher (R3)
Country: France
Application Deadline: 26 Dec 2024 - 22:00 (UTC)
Type of Contract: Temporary
Job Status: Full-time
Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
Is the Job related to staff position within a Research Infrastructure? No
Offer Description Title: "High-dimensional optimization of distributed assets in smart grids"
Context and background:
In the vertically integrated electrical energy systems of the past, power system management was carried out centrally by the transmission and distribution system operators (TSOs, DSOs). In the frame of the energy transition, emerging new actors (aggregators, microgrid operators, energy community managers, self-consumption etc.) and the proliferation of assets connected to the grid, such as renewable energy (RES) plants, storage devices, electric vehicles (EVs), smart-homes/prosumers with IoT devices, electric heating/cooling systems, etc., urge for a paradigm shift towards decentralization. New business models are likely to be based on physical or virtual groupings of assets ("cells"), instantiated as virtual power plants (VPPs), energy communities, microgrids, and others. In the power systems of the future, all these "cell" variants will probably coexist, and their operation should be optimized accounting for the specific interests of the involved actors. For example, a VPP operator aggregates hundreds to thousands of assets to achieve a critical mass of flexibility and valorize it in electricity markets. Optimization functions ("distributed intelligence") are necessary at these lower levels down to the grid edge (cell, feeders, assets/devices…) and also need to be aligned with the grid operation.
Scientific objectives:
The overarching objective of this research project is to develop distributed optimization methods for grids with a very large number (tens/hundreds of thousands to millions) of connected devices. The aim is to account for the involved uncertainties, the classification of assets/devices into different typologies of virtual/physical cells, their computational/communication capabilities/limitations, environmental disturbances, QoS/grid constraints, and privacy concerns. Large scale distributed optimization requires the design of appropriate grid-aware signals that affect the local optimization processes for a multitude of devices, so that their aggregation provides a predictable response while ensuring an optimal use of grid infrastructure.
Methodology and expected results:
The first step of the research project is a bibliographic research and familiarization with the methods and tools developed at our Group. The initial use case of focus will be the predictive management (scheduling) of the assets (for time frames in the order of a few minutes to a few days ahead). The developed approaches should be scalable to a very high number of assets, covering use cases such as distribution grids and/or VPPs that integrate EVs, RES plants, storage devices, prosumers and the like. The research project will integrate predictive models that reduce the complexity associated with multiple uncertainties, employ machine learning and/or statistical methods for high dimensional problems, and explore distributed optimization strategies to cope with the very large problem sizes.
Funding category: Autre financement public
Project: PEPR TASE "AI.NRGY - Distributed AI-based architecture of future energy systems integrating very large amounts of distributed sources"
PHD title: Doctorat en Énergétique et Procédés
PHD Country: France
Minimum Requirements:
Engineer and/or Master of Science degree (candidates may apply prior to obtaining their master's degree; the PhD will start after the degree is successfully 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 (e.g., R, Python, Julia,…). A successful candidate will have a solid background in three or more of the following competencies:
Optimization
Applied mathematics, statistics and probabilities
Machine learning, data science
Power systems, renewable integration
Expected level in French: Not required
Expected level in English: Proficiency
Desired starting date: As soon as possible in 2024 or early 2025.
Duration: 36 months.
Full-time paid position.
For more information please contact Prof. Georges Kariniotakis and Dr. Panagiotis Andrianesis.
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