Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning

Battery energy storage systems (BESSs) play a critical role in eliminating uncertainties associated with renewable energy generation, to maintain stability and improve flexibility of power networks. In this paper, a BESS is used to provide energy arbitrage (EA) and frequency regulation (FR) services...

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Autors principals:	Yushen Miao, Tianyi Chen, Shengrong Bu, Hao Liang, Zhu Han
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2021-12-01
Col·lecció:	Energies
Matèries:	battery energy storage energy arbitrage frequency regulation real-time market deep reinforcement learning
Accés en línia:	https://www.mdpi.com/1996-1073/14/24/8365
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