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

Author:

Zhang Huayue1,Wen Shuli2ORCID,Gu Mingchang2,Zhu Miao2,Ye Huili2

Affiliation:

1. College of Electrical Engineering Shanghai University of Electric Power Shanghai China

2. Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai Jiao Tong University Shanghai China

Abstract

AbstractIncreasing 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.

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

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