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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Bibliografiska uppgifter

Huvudupphov:	Yushen Miao, Tianyi Chen, Shengrong Bu, Hao Liang, Zhu Han
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	MDPI AG 2021-12-01
Serie:	Energies
Ämnen:	battery energy storage energy arbitrage frequency regulation real-time market deep reinforcement learning
Länkar:	https://www.mdpi.com/1996-1073/14/24/8365
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