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Title:

Safe Multi-Agent Reinforcement Learning for Price-Based Demand Response

Document type:
Konferenzbeitrag
Author(s):
Markgraf, Hannah; Althoff, Matthias
Abstract:
Price-based demand response (DR) enables house-holds to provide the flexibility required in power grids with a high share of volatile renewable energy sources. Multi-agent reinforcement learning (MARL) is a powerful, decentralized decision-making tool for autonomous agents participating in DR programs. Unfortunately, MARL algorithms do not naturally allow one to incorporate safety guarantees, preventing their real-world deployment. To meet safety constraints, we propose a safeguarding mechanism...     »
Keywords:
reinforcement learning, multi-agent systems, price-based demand response
Book / Congress title:
2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)
Publisher:
IEEE
Date of publication:
23.10.2023
Year:
2023
Covered by:
Scopus
Fulltext / DOI:
doi:10.1109/isgteurope56780.2023.10407281
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