Fueled by the evolution into the smart grid paradigm, ESTHER’s goal is to build analysis and design methods for modern power systems (i.e. MicroGrids) in the framework of Cyber-Physical Systems (CPSs), and to identify the best strategies for operating at the interconnections among the different levels of the hierarchical control pyramid. To overcome the limitations due to plug&play scenarios of renewable Distributed Energy Resources (DER) and Energy Storage Systems (ESS), new scalable and modulable control systems are targeted for considering the multi-level and multi-scale nature of power systems. The development of rigorous analytical theories founded on advanced mathematics is coupled with the development of dedicated simple algorithms that allow for a rapid deployment in numerical and experimental validation. The project is split in three phases/tasks: 1. The modeling of MicroGrids’ basic components, specifications and their interactions via platform-based design relying on the properties leveraged by contract theory; 2. The design of multi-level control methods that quantify the certificates of stability and subsequently the safe operating regions for MicroGrids; 3. The test of the developed controllers in real MicroGrids via Hardware-in-the-loop experiments.

Microalgae biofilm-based systems are emerging as a promising alternative to suspension cultivation methods. Indeed, their low energetic footprint, low water requirement and easy biomass harvesting procedure make them perfect candidates to be a robust alternative to plant crops for future commercial applications. One of the main bottlenecks that constrains the scaling-up of such systems is the lack of monitoring tools for biomass and molecules production. The main objectives of the MoBioSens project are to identify the best sensors for microalgae biofilm-based reactors monitoring and to implement calibration protocols for an on-line quantification of commercial biofilm traits such as biomass and high-value compound productivities. Several proximal sensors, including spectroscopy probes and multispectral cameras, will be tested and calibrated to be able to quantify microalgae biofilm traits growing in a rotating reactor. Sensor signals will be processed with standard statistical analysis and more complex predictive algorithms. Overall, the project will lay the foundations for the development of monitoring protocols of microalgae biofilm-based reactors for commercial applications.

Many real-life applications deal with preference-based assignments. In such multi-agent problems, agents have preferences over items (activities, resources, or even other agents), and these preferences must be aggregated into a collective decision which is an assignment of agents to these items. One can cite resource allocation or coalition formation problems. Nowadays, with the increasing use of algorithms and AI tools in systems governing our life choices (job recruitment, insurance covering, universities assignment), important decisions for the agents can be made in preference-based assignments. Therefore, to ensure confidence and participation in the system, it is crucial to guarantee that algorithms used for computing these assignments are fair to the agents. In a fair assignment, the decision should respect the expressed preferences of the agents and should not discriminate any population. However, fairness highly depends on the context of decision regarding, e.g., its temporality, the type of reported preferences, the type of assignment or the level of agents’ knowledge. Therefore, if one wants to realistically guarantee fairness in preference-based assignments, it is important to adapt fairness to the decision context while justifying that proposed solutions are indeed fair. This justification must intelligibly convince the agents that a fairer decision cannot be reached. The two properties of adaptability and explainability for fairness concepts will then together contribute to the adoption and trust by agents of systems using algorithms for assignment. The APPLE-PIE project proposes to investigate the guarantee of fairness via two axes: the design of flexible fairness concepts which are able to adapt to various decision contexts in order to tackle real-life contexts, as well as the explainability of fairness in proposed solutions. The project has also a practical dimension by planning to provide an explanation-oriented tool for computing fair assignments.

The fast transition to a carbon-neutral economy requires employing all the available levers to reduce greenhouse gas emissions, including using sustainable fuels for high-power facilities. Among the potential fuel molecules, ammonia (NH3) provides the highest energy density, can be easily transported, and its ideal combustion does not release any carbon. Still, the low flammability and the potential pollutant formation of NH3 combustion render its application difficult. In this project, plasma-assisted combustion using nanosecond repetitively pulsed (NRP) discharges is proposed to trigger new kinetics pathways and improve flame stabilization and pollutant reduction. These plasma kinetic pathways can be activated in two ways. In the first case, NRP discharges generate out-of-equilibrium plasmas, where N2 electronically excited states induce fast heating and molecular dissociation. A second regime, where NRP discharges abruptly transition to thermal equilibrium above 30,000 K, prompts intense hydrodynamic effects and complete dissociation, both beneficial for plasma-assisted combustion. Thus, the main objectives of this project are to apply these thermal and non-thermal NRP discharges in an NH3 burner and obtain a detailed understanding of their impact on pollutant formation and flame stabilization. The primary target will be to select baseline conditions in a new NH3 burner specifically designed for the ThermOnia project. Then, advanced diagnostics, namely tunable diode laser absorption spectroscopy (TDLAS or LAS) and calibrated emission spectroscopy (OES), will be applied. OES will be applied in the plasma vicinity to carefully evaluate the energy branching in non-equilibrium discharges (heating, formation of atomic species, …). In the thermal sparks, OES will also provide the degree of ionization and the associated heating. The plasma surrounding and the flame body will be probed by LAS to reconstruct the 3D distribution of species and temperature. This LAS and OES data will provide crucial insight into the interaction of the plasma and the flame. Comparing the effects of thermal and non-equilibrium discharges on the flame heat release and the pollutant formation will be pivotal in this project. Finally, this wealth of data will also be employed to validate and refine the EM2C phenomenological model of plasma-assisted combustion in non-equilibrium discharges, while extending it for thermal discharges.

From energy networks to space systems, complex autonomous systems have become pervasive in our society. In this context, the design of increasingly sophisticated methodologies for controlling these systems is of utmost relevance, given that they regularly operate in uncertain and dynamic circumstances. In particular, to mitigate hazardous and possibly catastrophic uncertain perturbations during the decision-making process, one must accurately design and reliably infuse stochastic dynamical models in the control pipeline. Moreover, to optimally balance robustness with respect to the aforementioned perturbations with performance, one must efficiently optimize complex rewards (or costs) online over spaces of controls, i.e., strategies, which are infinite-dimensional. These demanding desiderata call for the design of novel tools for the modelization and optimal control of stochastic systems. In this project, I will develop and combine original control-theoretic-based learning approaches with novel risk-averse stochastic optimal control techniques to tackle the relevant challenges underneath the modelization and optimal control of stochastic systems. The ultimate objective is to leverage such new methods to devise reliable and scalable algorithms for the efficient and safe-against-uncertainties deployment of autonomous systems in complex uncertain environments. This project is organized into three Work Packages (WP), which respectively aim at proposing reliable learning-based stochastic dynamical models (WP1), efficient resolution of risk-averse stochastic optimal control problems (WP2), and leveraging these scientific achievements to solve challenging application-related problems in space robotics and energy (WP3).