Energy storage resources must consider both price uncertainties and their physical operating characteristics when participating in wholesale electricity markets. This is a challenging problem as electricity prices are highly volatile, and energy storage has efficiency losses, power, and energy constraints. This paper presents a novel, versatile, and transferable approach combining model-based optimization with a convolutional long short-term memory network for energy storage to respond to or bid into wholesale electricity markets. We test our proposed approach using historical prices from New York State, showing it achieves state-of-the-art results, achieving between 70% to near 90% profit ratio compared to perfect foresight cases, in both price response and wholesale market bidding setting with various energy storage durations. We also test a transfer learning approach by pre-training the bidding model using New York data and applying it to arbitrage in Queensland, Australia. The result shows transfer learning achieves exceptional arbitrage profitability with as little as three days of local training data, demonstrating its significant advantage over training from scratch in scenarios with very limited data availability.

翻译：储能资源在参与批发电力市场时，必须同时考虑价格不确定性和自身物理运行特性。这是一个具有挑战性的问题，因为电价波动剧烈，且储能存在效率损耗、功率及能量约束。本文提出一种结合模型优化与卷积长短期记忆网络的新型、多功能、可迁移方法，用于储能设备响应或参与批发电力市场的竞价。我们使用纽约州的历史电价对提出的方法进行测试，结果显示该方法在不同储能时长下，无论是价格响应还是批发市场竞价场景中，均能达到最优性能，实现完美预见情境下70%至近90%的利润比。我们还通过测试迁移学习方法，利用纽约数据预训练竞价模型，并将其应用于澳大利亚昆士兰州的套利场景。结果表明，仅需三天本地训练数据，迁移学习即可实现卓越的套利盈利能力，在数据极其有限的情况下展现出相较于从零训练模型的显著优势。