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Titel:

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

Dokumenttyp:
Konferenzbeitrag
Autor(en):
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...     »
Stichworte:
reinforcement learning, multi-agent systems, price-based demand response
Kongress- / Buchtitel:
2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)
Verlag / Institution:
IEEE
Publikationsdatum:
23.10.2023
Jahr:
2023
Nachgewiesen in:
Scopus
Volltext / DOI:
doi:10.1109/isgteurope56780.2023.10407281
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