Dynamic energy management and control of networked microgrids based on load to grid services and incentive-based demand response programs: A multi-agent deep reinforcement learning approach

•Increase the speed of operational processing.•Creation of a competitive and encouraging platform based on L2 G services.•Increase the accuracy of data assessment.•Configuration of the IBDR structure to improve the load profile. This study has presented the energy management paradigm in a networked...

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Veröffentlicht in:	Sustainable cities and society Jg. 117; S. 105957
Hauptverfasser:	Seylab, Masoumeh Rezazadeh, Naderi, Mehdi S., Gharehpetian, Gevork B.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.12.2024
Schlagworte:	L2G services Multi-agent deep reinforcement learning algorithm Multi-task learning algorithm Networked microgrid Optimal operation Voltage and frequency control Multi-agent deep reinforcement learning algorithm L2G services Networked microgrid Optimal operation Voltage and frequency control Multi-task learning algorithm
ISSN:	2210-6707
Online-Zugang:	Volltext
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