Peter Verleijsdonck

Dr. Peter Verleijsdonck - TU/e, BiasLab

Improving Energy Management Systems to Address Congestion Challenges through Deep Reinforcement Learning and Bayesian Inference

Abstract: Grid congestion is accelerating the deployment of battery energy storage systems (BESS), which provide peak shaving during high-demand hours and grid-balancing services at other times. Balance Responsible Parties (BRPs) manage portfolios of BESS using smart energy management systems to deliver these services efficiently. In the imbalance market, BRPs trade deviations from scheduled generation or consumption to maintain system balance. Real-time price signals from the grid operator create financial incentives that can shorten BESS investment payback periods. In collaboration with Zympler, a BRP in the Netherlands, we are developing a dual AI approach to optimize energy trading for BESS. First, we use RxInfer to learn a generative model of market dynamics via Bayesian inference. Next, we apply DynaPlex to optimize battery operations through deep reinforcement learning based on the learned model. Using the complex price signals of the Dutch imbalance market as a case study, we demonstrate how this dual AI approach can accelerate return on investment.

Biography: Peter is a researcher in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e), where he works on probabilistic AI for flexible energy management. His current research focuses on developing Bayesian AI agents that optimize the use of flexible energy resources and enhance the reliability of the Dutch energy networks. He earned both his MSc and PhD in Applied Mathematics from TU/e. His work focuses on building models of reality grounded in data and physical interpretation to address everyday problems in logistics, maintenance, and computer science, with a particular focus on stochastic models and simulations for network, queueing, and decision-making problems. In addition, he has a deep interest in computer science and machine learning algorithms.

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