Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Technological challenges >> Smart Energy grids
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Thermal networks fault detection and localization on a citywide scale: an approach combining artificial intelligence and physical simulation

Département Thermique Biomasse et Hydrogène (LITEN)

Laboratoire des Systèmes Energétiques et Démonstrateurs Territoriaux

01-10-2020

SL-DRT-20-0990

yacine.gaoua@cea.fr

Smart Energy grids (.pdf)

Detection and localization of faults in thermal networks on a citywide scale: an approach combining artificial intelligence and physical simulation Over time, thermal networks (heating and cooling networks) age differently. Damages to underground pipelines, which can be invisible and not repaired, endanger not only the financial equilibrium of the network operator, but also the quality of heat supply to users, especially in winter periods. Thus, detecting hydraulic leaks and other anomalies remains one of the network operator's top priorities, as it requires a significant share of investment related to civil engineering works for locating and repairing leaks. The objective of this thesis is to implement an innovative diagnostic approach for the detection and localization of anomalies on the pipes of a heating network by exploiting network measurement data (data at the substation level and data from detectors positioned in the gutters) and the capabilities that artificial intelligence offers. The main objectives of this thesis are the following: - Generate database of normal and abnormal network operating modes using numerical simulation. This will be based on existing numerical models in the host laboratory. - Use numerical models for data validation and reconciliation to improve the quality of measurement data from a real network. - Identify algorithms for the detection and the localisation of anomalies based on machine learning algorithms (AI), data generated by simulation and measurement data from a real network. - Validate the performance of the algorithms for detecting anomalies on various simulated operating scenarios and then in real-life situations. - Implement a decision support tool for the detection and localization of anomalies for thermal networks One of the originalities of this work lies in the complementarity between the use of AI methods (supervised and unsupervised learning and classification) and detailed thermo-hydraulic models of networks. The latter should make it possible to compensate for two of the main pitfalls currently encountered by the use of AI methods: the strong dependence on the quality of measurement data, and the need to have very large databases with a high number of anomaly occurrences.

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Advanced solutions of management for multi-energy systems with high penetration of renewable energy resources

Département des Technologies Solaires (LITEN)

Laboratoire Systèmes Electriques Intelligents

01-10-2020

SL-DRT-20-1143

duy-long.ha@cea.fr

Smart Energy grids (.pdf)

The overall objective of this thesis is to decarbonise local multi-energy system by developing and demonstrating both technical and business solutions that can make it possible to integrate high shares of renewable energy sources. SOMENER will demonstrate the optimal integration of multi-energy vectors in local energy systems (energy islands) supported by high level of renewable energy sources and energy conversion technologies and storages of both short-term (e.g., battery, heat storage) and long-term (e.g., gas storage). The integration would not only enhance the efficiency within energy islands, but also have positive impacts on the existing connected centralized energy infrastructure, on local economy as well as on local environment. The focus of integration of multi-energy vectors would be to create new market mechanisms, business cases, and to develop the required technical supporting systems in which the concept of ?energy as a service?. One of the major challenges to decarbonise local energy systems, i.e., maximize the amount of renewable energy, is the mismatch between renewable energy production and the energy consumption by the customers on short-term scale (e.g., hours) and long-term scale (seasons) for electricity, heat and cooling, etc. SOMENER is focusing specifically on energy systems as a tool to address this challenge. The aim is to address this by i) integrating technologies for renewable energy sources, energy storages (e.g., electricity, heat, gas), and intelligent control technologies; ii) exploiting the synergies between energy systems, energy conversions and the flexibilities in energy uses (i.e., demand responses) in all energy vectors; iii) minimizing energy losses. SOMENER also focuses on the customer acceptance of technical solutions (e.g., advanced controls and management in multi-energy systems) and involvements of stakeholders in developing new business models and services which are of strong interest to both energy suppliers and customers. The project will provide integrated solutions for simulations, designs and optimized real-time operation and control of the energy islands as well as provide new business opportunities and opportunities for new actors and innovations. The core technical delivery of the project will be the SOMENER advanced integrated energy management system (IEM) which can be used to interconnect energy systems (electricity, heating, cooling, water, gas) and to optimise energy services in multi-time scale within energy islands. The IEM will enable secure operation of energy islands based on multi-energy co-simulation capabilities, advanced renewable energy and demand forecasting techniques, advanced scheduling and control of energy storages and demand-responses using optimal control techniques, such as model predictive control.

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