Deep reinforcement learning-based SOH-aware battery management for DER aggregation
Deep reinforcement learning-based SOH-aware battery management for DER aggregation
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DOI：
10.1145/3563357.3566166
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发表时间：
2022-11
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影响因子：
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通讯作者：
Shotaro Nonaka;Daichi Watari;Ittetsu Taniguchi;Takao Onoye
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文献类型：
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作者：
Shotaro Nonaka;Daichi Watari;Ittetsu Taniguchi;Takao Onoye
在智能能源系统中，电池在填补发电和消费之间的时间缺口方面发挥着重要作用，有望成为一种潜在的分布式能源(DER)。为了提取需求侧的灵活性，出现了一种收集各种DER的资源聚合器(RA)，并提出了基于强化学习的各种方法。由于电池在使用过程中不可避免地会出现退化，因此需要对电池进行管理，以将其降至最低。提出了一种基于深度强化学习的健康状态感知电池管理方法。我们的实验结果表明，平均电池寿命提高了11.2%。
In smart energy systems, batteries, which assume an important role in filling the temporal gap between generation and consumption, are expected to be a potential distributed energy resource (DER). A resource aggregator (RA) has emerged to collect various DERs to extract demand-side flexibility, and various methods have been proposed based on reinforcement learning. Since battery degradation is unavoidable during utilization, battery management is required to minimize it. This paper proposes state-of-health (SOH)-aware battery management based on deep reinforcement learning. Our experimental results demonstrate an average battery lifetime improvement of 11.2%.