In this smart charging webinar presented by Supergen Energy Networks hub and the EPSRC National Centre for Energy Systems Integration, we will discus realistic charging session models and reinforcement learning based control.
Introduction by Dr Myriam Neaimeh, Newcastle University and the Alan Turing Institute
Keynote speaker: Prof Dr Ir Chris Develder, Ghent University INTEC-imec
Topics for discussion:
Challenges that our modern power grid is facing include the increasing penetration of distributed renewable energy sources (DRES), as well as the electrification of transportation (i.e., electric vehicles). Part of the smart grid solution lies in demand response (DR) approaches to try and match the available production by adapting the flexibility in power consumption, e.g., shift consumption in time.
This presentation highlights research on electric vehicle (EV) charging that pertains to “knowing” the resulting power consumption, as a necessary condition for “controlling” it. For the “knowing” part, we will present results from data analytics on clustering and modeling user behavior in electric vehicle (EV) charging, in terms of total power consumption and the flexible portion thereof. We will introduce our recent models for generation of synthetic EV charging session data reflecting behavior from a large-scale real-world dataset. For the “controlling part”, we will present our reinforcement learning (RL) approach to jointly control a whole set of EV charging stations at once.
Speaker bio:
Chris Develder is an associate professor with the research group IDLab in the Department of Information Technology (INTEC) at Ghent University – imec, Ghent, Belgium. He received the MSc degree in computer science engineering and a PhD in electrical engineering from Ghent University (Ghent, Belgium), in July 1999 and December 2003 respectively (as a fellow of FWO). He has stayed as a research visitor at UC Davis, CA, USA (Jul.-Oct. 2007) and at Columbia University, NY, USA (Jan. 2013 – Jun. 2015). Chris currently leads two research teams within IDLab, (1) the AI for smart grids team (AI4SG) working on data analytics and machine learning for smart grids, and (2) the text-to-knowledge team (T2K) working on natural language processing (mostly information extraction using machine learning). Together with his teams, Chris has published well over 200 papers in international journals and conferences.
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