Resilience-Oriented Energy Management for All-Electric Ships Considering Safe Return

Marine power systems are isolated from external grids, making them more vulnerable than land-based power system. Additionally, different from fixed terrestrial microgrids, all-electric ships are mobile transportation vehicles, complying with the regulation of safe return to port. In order to enhance the resilience and improve energy efficiency of mobile microgrid, this study proposes a two-stage economic and resilient optimization framework for an all-electric ship (AES), taking into account the impact of navigation. In the first stage, a joint voyage and power scheduling of the AES is developed to reduce the operation costs and greenhouse gas emissions for normal operation mode. In the second stage, resilience-oriented optimization is proposed to defend the extreme contingency by optimizing the navigation speed and load shedding. Furthermore, resistance during the whole AES navigation is also considered. To verify the proposed algorithm, several cases are compared to demonstrate the resilience and economy of the shipboard power system and the necessity of addressing the effect of sailing resistance on the AES voyage.

[1]  Mehrdad Tarafdar Hagh,et al.  Preventive maintenance scheduling of multi energy microgrid to enhance the resiliency of system , 2021 .

[2]  Tianyang Zhao,et al.  Coordinated Optimal Energy Management and Voyage Scheduling for All-Electric Ships Based on Predicted Shore-Side Electricity Price , 2021, IEEE Transactions on Industry Applications.

[3]  N. Chang,et al.  Integrating emerging and existing renewable energy technologies into a community-scale microgrid in an energy-water nexus for resilience improvement , 2020 .

[4]  Nathan G. Johnson,et al.  Statistical development of microgrid resilience during islanding operations , 2020 .

[5]  Mahesh S. Illindala,et al.  Correlation-based feature selection for resilience analysis of MVDC shipboard power system , 2020, International Journal of Electrical Power & Energy Systems.

[6]  Milad Mehri Arsoon,et al.  Peer-to-peer energy bartering for the resilience response enhancement of networked microgrids , 2020 .

[7]  Zhengmao Li,et al.  Optimal Sizing of Shipboard Carbon Capture System for Maritime Greenhouse Emission Control , 2019, IEEE Transactions on Industry Applications.

[8]  Kate Anderson,et al.  Microgrid Resilience: A holistic approach for assessing threats, identifying vulnerabilities, and designing corresponding mitigation strategies , 2019, Applied Energy.

[9]  Mahesh S. Illindala,et al.  Risk-Based Mitigation of Load Curtailment Cyber Attack Using Intelligent Agents in a Shipboard Power System , 2019, IEEE Transactions on Smart Grid.

[10]  Hak-Man Kim,et al.  Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience , 2019, Applied Energy.

[11]  Mahesh S. Illindala,et al.  A distributed energy management strategy for resilient shipboard power system , 2018, Applied Energy.

[12]  Shengwei Mei,et al.  Resilience Control of DC Shipboard Power Systems , 2018, IEEE Transactions on Power Systems.

[13]  Mahesh S. Illindala,et al.  Graph Theory Based Shipboard Power System Expansion Strategy for Enhanced Resilience , 2018, IEEE Transactions on Industry Applications.

[14]  Surya Santoso,et al.  New Electric Shipboard Topologies for High Resiliency , 2018, IEEE Transactions on Power Systems.

[15]  Farrokh Aminifar,et al.  Networked Microgrids for Enhancing the Power System Resilience , 2017, Proceedings of the IEEE.

[16]  Mohammad Shahidehpour,et al.  Microgrids for Enhancing the Power Grid Resilience in Extreme Conditions , 2017, IEEE Transactions on Smart Grid.

[17]  Zhen Song,et al.  A resilience metric and its calculation for ship automation systems , 2016, 2016 Resilience Week (RWS).

[18]  Mahmud Fotuhi-Firuzabad,et al.  Enhancing Power System Resilience Through Hierarchical Outage Management in Multi-Microgrids , 2016, IEEE Transactions on Smart Grid.

[19]  C. L. Philip Chen,et al.  Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting , 2015, IEEE Transactions on Sustainable Energy.

[20]  Mohammad E. Khodayar,et al.  Resilient operation of multiple energy carrier microgrids , 2015, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[21]  Kit Po Wong,et al.  Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine , 2014, IEEE Transactions on Power Systems.

[22]  Xiaofeng Meng,et al.  Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction , 2014, IEEE Transactions on Power Systems.

[23]  P. Krishnankutty,et al.  Ship Resistance and Propulsion , 2013 .

[24]  John E. Dennis,et al.  Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems , 1998, SIAM J. Optim..

[25]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[26]  Zhengjiang Liu,et al.  An analysis of factors affecting the severity of marine accidents , 2021, Reliab. Eng. Syst. Saf..

[27]  Tao Ding,et al.  Defense Strategy for Resilient Shipboard Power Systems Considering Sequential Attacks , 2020, IEEE Transactions on Information Forensics and Security.

[28]  Radoslav Radonja THE PREVENTION OF AIR POLLUTION FROM SHIPS , 2019 .