Game theory-based electricity pricing and microgrids management using online deep reinforcement learning

This study addresses a bi-level problem involving a retailer and multiple residential microgrids. The retailer, at the upper level, disseminates selling and buying electricity price signals to maximize profit, while microgrid agents, at the lower level, manage their resources based on these signals...

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Veröffentlicht in:	Applied soft computing Jg. 182; S. 113621
Hauptverfasser:	Shademan, Mahdi, Azizi, Ali, Jadid, Shahram
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.10.2025
Schlagworte:	Deep reinforcement learning Demand response Fuzzy chance constrained programming Microgrids Model-drift Resource management Model-drift Fuzzy chance constrained programming Deep reinforcement learning Demand response Resource management Microgrids
ISSN:	1568-4946
Online-Zugang:	Volltext
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