Energy supervision of distribution networks with connected electric vehicles, renewable energy sources, storage systems and controllable passive loads

Electricity distribution grids have been for several years at the heart of energy and environmental challenges affecting our societies. Customers are increasingly switching from instantaneous gas water heaters to electric hot water tanks, and from fossil fuel-powered vehicles to electric vehicles. On the other hand, renewable energy production is starting to become an important part of the energy mix but does not necessarily match the electricity consumption of the users. It can therefore be transmitted to the rest of the distribution network, and eventually the transmission network. All these evolutions must be controlled to avoid significant reinforcements of the network infrastructures and limit the economic and environmental impacts of the energy transition. In this context, this thesis focuses on the energy supervision of medium voltage distribution grids, particularly rural and suburban types, to promote the local consumption of renewable energy and optimize energy transmission costs for network operators. The developed algorithms mainly aim to control loads such as electric vehicles and electric water heaters. The supervision system acts at the level of an HV/MV substation and uses real-time production and consumption measurements as well as day-ahead forecasts to control the various available loads. The proposed strategy also takes into account the electrical constraints that can appear on the network through a co-simulation approach between Matlab/Simulink software, where the supervision strategy is implemented, and DIgSILENT PowerFactory, where the distribution network is finely modeled. The supervision's power set-points are therefore tested and adapted in real-time before being sent to the various customers. Finally, the control of decentralized producers is used as an additional lever to solve a part of the detected electrical constraints. (author)