Optimal power and energy management control for hybrid fuel cell-fed shipboard DC microgrid
Chen, W.J.; Tai, K.; Lau, M.W.S.; Abdelhakim, A.; Chan, R.R.; Adnanes, A.K.; Tjahjowidodo, T. (2023). Optimal power and energy management control for hybrid fuel cell-fed shipboard DC microgrid. Ieee Transactions on Intelligent Transportation Systems 24(12): 14133-14150. https://dx.doi.org/10.1109/TITS.2023.3303886 In: Ieee Transactions on Intelligent Transportation Systems. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC: Piscataway. ISSN 1524-9050; e-ISSN 1558-0016, more |  |
| Keyword | | | Author keywords | All-electric ship; fuel cell system; hybrid shipboard power microgrid; model predictive control; reinforcement learning; power management control |
| Authors | | Top | - Chen, W.J.
- Tai, K.
- Lau, M.W.S.
- Abdelhakim, A.
| - Chan, R.R.
- Adnanes, A.K.
- Tjahjowidodo, T., more
| |
| Abstract | The all-electric ship (AES) with DC-grid configuration has demonstrated advantages compared to the traditional AC system and has become the state-of-the-art for ships with electric propulsion in the low to medium power range during the past decade. However, the integration with different power sources, such as fuel cells, batteries and diesel gen-sets, increases the system complexity and requires an advanced power management system (PMS) to handle vessel operation and to achieve optimal power control. This paper presents an optimized power management strategy to reduce the total cost of ownership of such vessels, considering not only the fuel cost and emission penalty, but also the power device degradation and equipment replacement cost. In this study, Model Predictive Control (MPC) and Reinforcement Learning (RL)-based PMS control methods are approached respectively. In order to demonstrate the performance of MPC and RL techniques, a typical tugboat load profile is simulated. The testing results are also compared with a traditional rule-based power management control. |
|
