A reinforcement learning method for two-layer shipboard real-time energy management considering battery state estimation

IF 1.6 Q4 ENERGY & FUELS
Huayue Zhang, Shuli Wen, Mingchang Gu, Miao Zhu, Huili Ye
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引用次数: 0

Abstract

Increasing global environmental concerns encourage a continuous reduction in carbon emissions from the shipping industry. It has become an irreversible trend to replace traditional fossil fuels with advanced energy storage technology. However, an improper energy management leads to not only energy waste but also undesired costs and emissions. Accordingly, the authors develop a two-layer shipboard energy management framework. In the initial stage, a shipboard navigation planning problem is formulated that considers battery state estimation and is subsequently solved using particle swarm optimisation to obtain an optimal speed trajectory. To track the scheduled speed, a reinforcement learning method based on a deep Q-Network is proposed in the second stage to realise real-time energy management of the diesel generator and energy storage system. This approach ensures that the state of charge remains within a safe range and that the performance is improved, avoiding excessive discharge from the energy storage systems and further enhancing the efficiency. The numerical results demonstrate the necessity and effectiveness of the proposed method.

Abstract Image

考虑电池状态估计的双层舰载实时能源管理强化学习方法
全球对环境的日益关注促使航运业不断减少碳排放。用先进的储能技术取代传统化石燃料已成为不可逆转的趋势。然而,不恰当的能源管理不仅会导致能源浪费,还会产生不必要的成本和排放。因此,作者开发了一个双层船上能源管理框架。在初始阶段,提出了一个考虑电池状态估算的船载导航规划问题,随后使用粒子群优化法进行求解,以获得最佳速度轨迹。为了跟踪预定速度,第二阶段提出了一种基于深度 Q 网络的强化学习方法,以实现柴油发电机和储能系统的实时能量管理。这种方法可确保充电状态保持在安全范围内,并提高性能,避免储能系统过度放电,进一步提高效率。数值结果证明了所提方法的必要性和有效性。
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来源期刊
IET Energy Systems Integration
IET Energy Systems Integration Engineering-Engineering (miscellaneous)
CiteScore
5.90
自引率
8.30%
发文量
29
审稿时长
11 weeks
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