Temporal-Aware Deep Reinforcement Learning Enhances Energy Storage Bidding in Electricity Markets

Konsep Inti

The author employs deep reinforcement learning to optimize bidding strategies for energy storage systems in multiple electricity markets, enhancing revenue potential and grid stability.

Abstrak

The content discusses the application of deep reinforcement learning (DRL) in optimizing bidding strategies for battery energy storage systems (BESS) participating in spot and contingency frequency control ancillary services (FCAS) markets. The study highlights the importance of temporal-aware bidding behavior and interpretable DRL models in maximizing profits and supporting grid reliability. By leveraging a transformer-based temporal feature extractor, the proposed method outperforms benchmarks by substantial margins across various jurisdictions within the Australian National Electricity Market.
The study addresses gaps in existing research by focusing on joint bidding strategies, capturing historical price trends, and providing insights into BESS behaviors. The results demonstrate significant revenue boosts compared to traditional optimization-based approaches and highlight the potential of DRL in enhancing market participation efficiency.
Key points include:

BESS participation in multiple electricity markets for revenue maximization.
Importance of temporal information extraction for effective bidding decisions.
Comparison with optimization-based methods and other DRL strategies.
Revenue increases achieved through joint-market participation using TempDRL.

Statistik

Our strategy outperforms benchmarks by substantial margins.
Effective temporal-aware bidding significantly increases profits.
Joint bidding can unlock the full potential of BESS.
Proposed method leverages a transformer-based temporal feature extractor.

Kutipan

"Our approach leverages a transformer-based temporal feature extractor to effectively respond to price fluctuations."
"Our strategy outperforms benchmarks, including both optimization-based and other DRL-based strategies, by substantial margins."

Wawasan Utama Disaring Dari

Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets

by Jinhao Li,Ch... pada arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19110.pdf

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