Reliable Power Scheduling of an Emission-Free Ship: Multi-Objective Deep Reinforcement Learning

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Environmental pollutants as a global concern has led to a general increase in the utilization of renewable energy resources instead of fossil fuels. Accordingly, the penetration of these resources in allelectric ships, as well as power grids, has increased in recent years. In this paper, in order to have a zeroemission and cost-effective energy management in an allelectric ferry boat, a new reliable and optimal power scheduling is presented that uses fuel cell and battery energy storage systems. Furthermore, the real information including load profile and paths is considered for the case study to assess the feasibility and superiority of the proposed approach. In addition to the cost of energy management, to have a reliable combination of the proposed resources, loss of load expectation (LOLE) as a reliability index is considered in energy management context and the problem is solved by the deep reinforcement learning in a multi-objective manner. The results of the consideration of two common standards including DNVGL-ST-0033 and DNVGL-ST-0373 demonstrate that the proposed energy management method is applicable in industrial applications. Finally, the real-time simulation-based hardware-in-the-loop (HIL) is conducted to validate the performance and efficacy of the suggested power scheduling for the emission-free ships.

TidsskriftIEEE Transactions on Transportation Electrification
Sider (fra-til)832-843
Antal sider12
StatusUdgivet - jun. 2020

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