Description
Organizers and chairs: Sandjai Bhulai and Alessandro Zocca
Presentation materials
This talk introduces a decentralized Multi-Agent Reinforcement Learning (MARL) with inverse reinforcement learning for Electricity Demand Response (DR) programs in the residential sector, aiming at grid stability and efficiency while prioritizing user privacy. The approach is for incentive-based DR that addresses the grid's capacity limits and congestion challenges and aligns with the...
Recent challenges in operating power networks arise from increasing energy demands and unpredictable renewable sources like wind and solar. While reinforcement learning (RL) shows promise in managing these networks, through topological actions like bus and line switching, efficiently handling large action spaces as networks grow is crucial. In this talk we present a hierarchical multi-agent...
Power network control is a crucial aspect of modern society, as it allows electricity to be a reliable resource for daily living, industry, and transportation. Controlling electricity is a highly complex task that represents a sequential decision-making problem with large state and action spaces. The state space represents a combinatorial explosion of all possible ways the network can be...
