Conception and deployment of innovative optimal control strategies for smart energy grids
District heating networks (DHNs) play a vital role in energy transition strategies due to their ability to integrate renewable and waste heat effectively. In France, the national low-carbon strategy emphasizes expanding and optimizing DHNs, including smaller networks with multiple heat sources like solar thermal and storage. Smart control systems, such as model-predictive control (MPC), aim to replace manual, expert-based practices to enhance efficiency. However, deploying advanced control systems on small DHNs remains challenging due to the cost and complexity of hardware and maintenance requirements.
Current industrial solutions for large DHNs leverage mixed-integer linear programming (MILP) for real-time optimization, while smaller networks often rely on rule-based systems. Research efforts focus on simplifying MPC models, utilizing offline pre-calculations, or incorporating machine learning to reduce complexity. Comparative studies assess various control strategies for adaptability, interpretability, and operational performance.
This postdoctoral project aims to advance DHN control strategies by developing, testing, and deploying innovative approaches on a real DHN experimental site. It involves creating and comparing control models, implementing them in a physical simulator, and deploying the most promising solutions. Objectives include optimizing operational costs, improving system robustness, and simplifying deployment while disseminating findings through conferences, publications, and potential patents. The researcher will have access to cutting-edge tools, computational resources, and experimental facilities.
Study of the specific features of highly distributed architectures for decision and control requirements
Our electricity infrastructure has undergone and will continue to undergo profound changes in the coming decades. The rapid growth in the share of renewables in electricity generation requires solutions to secure energy systems, especially with regard to the variability, stability and balancing aspects of the electricity system and the protection of the grid infrastructure itself. The purpose of this study is to help design new decision-making methods, specially adapted to highly distributed control architectures for energy networks. These new methods will have to be evaluated in terms of performance, resilience, robustness and tested in the presence of various hazards and even byzantines.
Modeling of faults on low voltage DC networks in buildings, towards fault detection algorithms
The development of the use of renewable energies and energy storage as well as the progress made by power electronic components are gradually leading to a rethinking of the architectures of low voltage electrical distribution networks in buildings. These developments will allow the development of direct current or mixed alternating-direct current networks supplied by static converters. On this type of network, faults become more difficult to manage due to the power sources used. Indeed, the usual signatures of the short-circuit or the overload are no longer the same and will vary according to the converters used and the architecture of the network. For this, it is necessary to identify, by simulation, the most suitable protection topologies (by neutral systems for example) and to identify the typical fault signatures. Ultimately, these signatures will provide optimum detection devices.
Formalization of the area of responsibility of the actors of the electricity market
The CEA is currently developing a simulation tool which models the energy exchanges between the actors of the electricity market but which models, in addition, the exchanges of information between those actors. The first results of this work show that, for some new energy exchange schemes, ’indirect’ interactions between actors may appear and may cause financial damage (for example, the failure of a source of production of one actor may impact the income of another). Thus, the borders which clearly delimited until now the areas of responsibility of each actors could be brought to blur and their areas of responsibility could "overla". The candidate will be responsible for:
- Formally define the area of responsibility of an actor in the electricity market,
- Model the interactions, including ’indirect’ ones, that may appear between these actors,
- Apply formal proof techniques (such as ’model-checking’) to detect overlaps in areas of responsibility,
- Define the conditions of exchange between the actors which would guarantee the non-recovery of the areas of responsibility.
Attack detection in the electrical grid distributed control
To enable the emergence of flexible and resilient energy networks, we need to find solutions to the challenges facing these networks, in particular digitization and the protection of data flows that this will entail, and cybersecurity issues.
In the Tasting project, and in collaboration with RTE, the French electricity transmission network operator, your role will be to analyze data protection for all parties involved. The aim is to verify security properties on data in distributed systems, taking into account that those induce a number of uncertainties.
To this end, you will develop a tool-based methodology for protecting the data of power grid stakeholders. The approach will be based on formal methods, in particular runtime verification, applied to a distributed control system.
This postdoc position is part of the TASTING project, which aims to meet the key challenges of modernizing and securing power systems. This 4-year project, which started in 2023, addresses axis 3 of the PEPR TASE call “Technological solutions for the digitization of intelligent energy systems”, co-piloted by CEA and CNRS, which aims to generate innovations in the fields of solar energy, photovoltaics, floating wind power and for the emergence of flexible and resilient energy networks. The targeted scientific challenges concern the ICT infrastructure, considered as a key element and solution provider for the profound transformations that our energy infrastructures will undergo in the decades to come.
The project involves two national research organizations, INRIA and CEA through its technological research institute CEA-List. Also involved are 7 academic laboratories: G2Elab, GeePs, IRIT, L2EP, L2S and SATIE, as well as an industrial partner, RTE, which is supplying various use cases.