Federated Predictive Management of Multi-Energy Systems with Privacy Preservation

Context and challenges:

The decarbonization of energy systems is driving the increasing electrification of end uses and the large-scale integration of renewable generation, energy storage, and distributed energy resources. This transformation is also leading to the emergence of multi-energy systems, in which electricity, thermal, and gas/hydrogen networks are increasingly coupled and need to be operated in a coordinated manner. Such systems may range from energy communities and microgrids to residential districts, industrial clusters, and larger territorial energy systems. Their efficient operation requires anticipating and coordinating generation, consumption, storage, and flexible loads over time horizons ranging from a few minutes to several days. At the same time, they must dynamically respond to external grid and market signals, such as flexibility requests, dynamic electricity tariffs, network constraints, or extreme events.

A major challenge arises from the distributed and multi-actor nature of these systems. The data required for their efficient management are owned by different stakeholders and may contain sensitive or confidential information. Centralizing such data is therefore not always possible or desirable. Furthermore, the different actors may pursue distinct, and potentially conflicting, objectives, requiring coordination mechanisms that preserve their autonomy while enabling efficient operation of the overall system. This creates a need for distributed and federated AI approaches that allow multiple actors to collaborate without directly sharing sensitive operational data. Beyond predictive accuracy and operational performance, these approaches must remain effective under missing, corrupted, or incomplete information and address key requirements of trustworthy AI, including privacy, explainability, uncertainty quantification, and robustness.

Main objective of the thesis:

The main objective of the PhD is to develop a distributed, federated framework for predictive and prescriptive management of multi-energy systems, enabling coordination among multiple actors while preserving the privacy and confidentiality of their data. The research will combine predictive models, for instance, for forecasting energy demand, renewable generation, available flexibility, or future system states, with prescriptive methods determining appropriate actions for generation, storage and flexible consumption.

Particular emphasis will be placed on Federated Learning (FL), enabling multiple actors to collaboratively train models without centralising their operational data. The research will investigate how FL can go beyond conventional forecasting applications and become part of an integrated decision-making framework linking prediction with the coordinated operation of distributed energy resources. Coordination among actors will be addressed through a multi-agent architecture and distributed coordination mechanisms, including approaches based on game theory and distributed optimisation. These methods will account for actors with different, and potentially competing, objectives while seeking operating solutions compatible with the overall performance and constraints of the multi-energy system.

The central research question can therefore be formulated as follows: How can federated learning and distributed AI enable coordinated  management of multi-energy systems without centralising stakeholders' sensitive data, while maintaining reliable performance under uncertain and degraded operating conditions?

Methodology and expected results:

The PhD will investigate new AI-based approaches for the predictive management of multi-energy systems involving multiple actors and energy resources. Representative applications may include energy communities, residential districts, microgrids, or industrial energy systems.

A central part of the research will focus on how different actors can learn from their collective experience and coordinate their decisions without directly sharing sensitive operational data. The candidate will explore federated and distributed AI approaches to improve the prediction of energy needs and available flexibility and to support the coordinated management of generation, storage, and flexible consumption. The research will also investigate how these approaches can remain reliable when operating conditions are uncertain or when available information is incomplete. Particular attention will be given to the robustness, privacy, and trustworthiness of the proposed solutions.

The developed methods will be evaluated through realistic multi-energy use cases and integrated into an open-source simulation environment. The expected outcome is a methodological framework for privacy-preserving, predictive management of multi-energy systems, together with models and tools that can support their evaluation under a variety of operating conditions.

Collaborations:This thesis is supported by the PEPR program FutuRE funded underFrance 2030: 

• https://lnkd.in/p/eSkjy3HV
• https://lnkd.in/p/eVNGjMyU  

The project will be also linked to the European project Fair4Communities  (2027-2030) coordinated by our Group. 

MINES PARIS - PSL, Centre PERSEE

The PERSEE Center is one of the 18 research centers of MINES Paris. Its field of expertise concerns New Energy Technologies and Renewable Energy Sources (RES). Its research strategy is based on a "micro/macro" approach ranging from (nano)materials to energy systems. It is built around three structuring themes: i) materials and components for energy, ii) sustainable energy conversion and storage processes and technologies, and iii) renewable energies and smart energy systems.This late is developped by one of the three groups of the Center, ERSEI, which stands for “Renewable Energies and Smart Energy Systems”.

The ERSEI group develops methods and tools allowing the optimal integration of decentralized sources, including RES, storage devices, electric vehicles, active demand and other technologies, in energy systems and electricity markets. The research activity of the group is developped through three main axes. The first is based on the development of advanced short-term forecasting methods for different applications in power systems (i.e. forecasting of RES production, demand, dynamic line rating, market quantities, etc.). The second concerns the control and predictive management of energy systems. The aim is to design innovative approaches to optimise the operation (from real-time to days ahead) of different types of systems (smart-homes, microgrids, virtual power plants, energy communities, hybrid RES/storage plants, distribution grids multi-energy systems a.o.) considering uncertainties. The third axis concern planning and prospective studies that aim to optimise the design of future energy systems, generate furture scenarios, optimise investements etc.

The PERSEE Center is located within the scientific parc of Sophia-Antipolis, near the cities of Nice, Cannes and Antibes in the south of France. Its workforce is around 55 persons.

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.  A succesful candidate will have a solid background in two or more of the following competencies:

• artificial intelligence, data science, machine learning
• applied mathematics, statistics and probabilities
• optimisation
• power systems

Expected level in French : bon niveau souhaitable

Expected level in English : excellent