Trustworthy AI for Predictive Control of Multi-Energy Systems

MINES Paris PSL

Valbonne

Sur place

EUR 20 000 - 27 000

Plein temps

Il y a 4 jours

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Avantages offerts par ce poste

PhD funding

Real-time simulation facilities

Résumé du poste

Mines Paris-PSL invites applications for a PhD thesis on Trustworthy AI for Predictive Control of Multi-Energy Systems. The project develops a safe, real-time AI framework coordinating energy vectors with storage and conversion technologies.

Researchers will build a digital twin simulation, explore learning-based control, and validate through Hardware-in-the-Loop experiments, aiming for robust, uncertainty-aware decisions.

Qualifications

Engineer or MSc with strong background in control and AI for energy systems.
PhD start can be after degree; applicants may apply before completing their Master's.

Responsabilités

Develop a trustworthy AI framework for predictive control of multi-energy systems.
Build a simulation framework (digital twin) for diverse operating scenarios.
Validate methods via real-time simulation and Hardware-in-the-Loop experiments.

Connaissances

Multi-energy systems

Trustworthy AI

Predictive control

Reinforcement learning

Physics-informed AI

Real-time simulation

Hardware-in-the-Loop

Energy flexibility

Formation

Master of Science in engineering or science

Outils

Real-time simulation tools

Digital twin concepts

Description du poste

Trustworthy AI for Predictive Control of Multi-Energy Systems

21/09/2026 Autre financement public

Mines Paris-PSL

Trustworthy AI for Predictive Control of Multi-Energy Systems

Energie

Multi-energy systems, Trustworthy AI, Predictive control, Reinforcement learning, Physics-informed AI, Energy flexibility, Real-time simulation, Hardware-in-the-Loop

Context and challenges:

The transition towards low-carbon energy systems is leading to a growing integration of renewable generation, storage and flexible demand, together with stronger interactions between electricity, heat, gas and hydrogen infrastructures. Technologies such as heat pumps, electrolysers, fuel cells, batteries and other Power-to-X and X-to-Power solutions make it possible to transfer energy and flexibility across different energy vectors. This coupling creates new opportunities for improving the efficiency, resilience and flexibility of energy systems, but also significantly increases the complexity of their operation.

Future multi-energy systems will have to coordinate a large number of distributed and heterogeneous resources while dealing with renewable generation and demand uncertainty, network constraints, disturbances and dynamics occurring over different time scales. Conventional model-based control and optimisation approaches may become computationally demanding when applied in real time to such complex systems. AI, and particularly learning-based control, offers promising alternatives by learning efficient control strategies from large numbers of simulated or observed operating situations. However, the use of AI for controlling critical energy infrastructures raises important questions concerning trustworthiness and safety. Decisions proposed by AI must respect the physical constraints of the different energy networks, remain feasible under uncertain or previously unseen situations, and provide sufficient guarantees regarding robustness and reliability. Developing AI methods that combine learning capabilities with physical knowledge and safety requirements is therefore a major research challenge.

Main objective of the thesis:

The main objective of the thesis is to develop a trustworthy AI framework for the predictive control of multi-energy systems ensuring a safe operation in real-time, considering interactions between different energy vectors, renewable generation, storage and energy conversion technologies.

The developed approaches will aim to exploit the flexibility available across the different energy infrastructures while respecting their physical and operational constraints. Particular attention will be paid to the ability of the proposed methods to operate under uncertainty and disturbances and to provide control decisions within computational times compatible with real-time operation. Beyond performance, the thesis will investigate how trustworthiness can be incorporated into AI-based control by combining physical knowledge, uncertainty awareness, robustness and explicit safety mechanisms.

Methodology and expected results:

The research will first establish a simulation framework representing the main dynamics, constraints and interactions of the considered multi-energy system. This environment will act as a digital twin, enabling the generation of diverse operating scenarios covering normal conditions as well as uncertain, stressed or disturbed situations. It will provide a controlled environment for developing, training and systematically evaluating AI-based predictive control strategies for horizons near real-time ranging e.g. from seconds to minutes ahead, compatible with feasible and safe operation of the multi-energy system in real-time considering dynamic events (e.g. in terms of frequency or voltage fluctuations or local faults of electrical grids, fast variations in heat or gas networks).

Building on this framework, the thesis will investigate learning-based control methods, including reinforcement learning and approaches capable of exploiting the network structure and interactions between energy vectors. Different ways of incorporating physical knowledge and operational constraints into the learning process will be explored in order to improve data efficiency, generalization and physical feasibility of the resulting decisions. Distributed control approaches may also be investigated to coordinate resources located in different parts of the system while limiting the amount of information that needs to be exchanged. A specific part of the research will address the trustworthiness of AI-based control, including the assessment of uncertainty, robustness to operating conditions not encountered during training, explainability of control decisions and the definition of safe operating boundaries. The objective will be to identify when an AI controller can be trusted and mechanisms allowing the system to remain within acceptable operating conditions when this cannot be guaranteed.

Finally, the proposed approaches will be validated through real-time simulation and Hardware-in-the-Loop experiments, allowing AI controllers to interact with realistic simulations and physical or emulated equipment. The expected outcome is an integrated methodology for developing and evaluating trustworthy AI controllers for future multi-energy systems, together with quantitative evidence of their performance, robustness, computational efficiency and safety under realistic operating conditions.

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

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

01/11/2026

Mines Paris-PSL

The PERSEE Centeris one of the 18 research centers ofMINESParis. Itsfield 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, iii) renewable energies and smart energysystems.This late is developped by one of thethree groups of theCenter,ERSEI, which stands for “Renewable Energies and Smart Energy Systems”.

The ERSEI group developsmethods 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 advancedshort-term forecasting methodsfor different applications in power systems (i.e. forecasting of RES production, demand, dynamic line rating, market quantities, etc.). The second concerns thecontrol and predictive management of energy systems. The aim is to design innovative approaches to optimise theoperation (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 axisconcernplanning and prospective studiesthat aim to optimise the design of future energy systems,generate furture scenarios, optimise investements etc.

The PERSEE Center is located within the scientific parcof Sophia-Antipolis, near the cities of Nice, Cannes and Antibes in the south of France. Its workforce is around55persons.

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: