Application of Machine Learning and Mathematical Programming in the Optimization of the Energy Management System for Hybrid-Electric Vessels Having Cyclic Operations
Creators
- 1. Politecnico di Milano, Italy
- 2. Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland
- 3. Damen shipyard, the Netherlands
Description
Shipping contributes today to 2.1% of global anthropogenic greenhouse gas emissions and its share is expected to grow in the coming years. At the same time, fuel prices are increasing and companies of the related increase in operational costs. This demands for higher efficiency in ship operations. In these regards, batterypowered vessels are often regarded as a promising solution. The existence of an energy storage element in the system, however, introduces additional challenges in its efficient control.
This paper presents the application of machine learning and mathematical programming to the optimization of the energy management system of Diesel-electric vessels with an energy storage system operating according to a cyclical operational profile.
The proposed energy management system uses unsupervised exclusive machine learning algorithms,k-means or k-medoids, to learn from prior operations. Then mathematical programming based on mixed-integer linear programming is used to address the problem of the optimal unit commitment by means of optimizing the system’s operations for minimizing fuel consumption. The calculated optimal state of charge of the energy storage system is used as the reference value for a proportional-integral controller during the real-time operations.
The proposed energy management system is evaluated through its application to a case study corresponding to a hybrid-electric ferry operating in a urban area having cyclic operations through several stations. The results show that the efficiency of the control action is high with an accuracy ranging between 87% and 99%, when compared to an ideal controller, even in presence of large variations in the operational profile and the charging stations.
Between the two tested clustering algorithms, k-means showed higher efficiency in the reduction of fuel consumption in presence of charging stations, while in absence of these, k-medoids showed to provide a better performance.
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INEC 2018 Paper 064 Mohammadzadeh SDG FINAL.pdf
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Additional details
References
- Martin Stopford. Maritime economics. Routledge, 2013.
- Mohd R Mohamed, Suleiman M Sharkh, and Frank C Walsh. Redox flow batteries for hybrid electric vehicles: Progress and challenges. In Vehicle Power and Propulsion Conference, 2009. VPPC'09. IEEE, pages 551–557. IEEE, 2009.
- Robert Alvarez, Peter Schlienger, and Martin Weilenmann. Effect of hybrid system battery performance on determining co2 emissions of hybrid electric vehicles in real-world conditions. Energy Policy, 38(11):6919– 6925, 2010.
- Arthur Vrijdag, D Stapersma, and T Van Terwisga. Control of propeller cavitation in operational conditions. Journal of Marine Engineering & Technology, 9(1):15–26, 2010.
- Viknash Shagar, Shantha Gamini Jayasinghe, and Hossein Enshaei. Effect of load changes on hybrid shipboard power systems and energy storage as a potential solution: A review. Inventions, 2(3):21, 2017.
- Jun Hou, Jing Sun, and Heath Hofmann. Mitigating power fluctuations in electrical ship propulsion using model predictive control with hybrid energy storage system. In American Control Conference (ACC), 2014, pages 4366–4371. IEEE, 2014.
- Bijan Zahedi, Lars E. Norum, and Kristine B. Ludvigsen. Optimized efficiency of all-electric ships by dc hybrid power systems. Journal of Power Sources, 255:341–354, June 2014.
- Amjad Anvari-Moghaddam, Tomislav Dragicevic, Lexuan Meng, Bo Sun, and Josep M Guerrero. Optimal planning and operation management of a ship electrical power system with energy storage system. In Industrial Electronics Society, IECON 2016-42nd Annual Conference of the IEEE, pages 2095–2099. IEEE, 2016.
- R D Geertsma, R R Negenborn, K Visser, and J J Hopman. Design and control of hybrid power and propulsion systems for smart ships: A review of developments. Applied Energy, 194:30–54, 2017.
- Jingang Han, Jean-Frederic Charpentier, and Tianhao Tang. An energy management system of a fuel cell/battery hybrid boat. Energies, 7(5):2799–2820, 2014.
- CristianMusardo,GiorgioRizzoni,YannGuezennec,andBenedettoStaccia. A-ecms: An adaptive algorithm for hybrid electric vehicle energy management. European Journal of Control, 11(4-5):509–524, 2005.
- Yu Wang and Zongxuan Sun. Dynamic analysis and multivariable transient control of the power-split hybrid powertrain. IEEE/ASME Transactions on Mechatronics, 20(6):3085–3097, 2015.
- Ameen M Bassam, Alexander B Phillips, Stephen R Turnock, and Philip A Wilson. Development of a multi-scheme energy management strategy for a hybrid fuel cell driven passenger ship. International Journal of Hydrogen Energy, 42(1):623–635, 2017.
- E Barklund, Nagaraju Pogaku, Milan Prodanovic, C Hernandez-Aramburo, and Tim C Green. Energy management in autonomous microgrid using stability-constrained droop control of inverters. IEEE Transactions on Power Electronics, 23(5):2346–2352, 2008.
- FD Kanellos. Optimal power management with ghg emissions limitation in all-electric ship power systems comprising energy storage systems. IEEE Transactions on Power Systems, 29(1):330–339, 2014.
- Bijan Zahedi, Lars E Norum, and Kristine B Ludvigsen. Optimized efficiency of all-electric ships by dc hybrid power systems. Journal of power sources, 255:341–354, 2014.
- Espen Skjong, Tor Arne Johansen, Marta Molinas, and Asgeir J Sørensen. Approaches to economic energy management in diesel–electric marine vessels. IEEE Transactions on Transportation Electrification,3(1):22– 35, 2017.
- Hugo Grimmelius, Peter de Vos, Moritz Krijgsman, and Erik van Deursen. Control of hybrid ship drive systems. In 10th International conference on computer and IT applications in the maritime industries, pages 1–15, 2011.
- Gayathri Seenumani. Real-time power management of hybrid power systems in all electric ship applications. 2010.
- Brian Everitt, editor. Cluster analysis. Wiley series in probability and statistics. Wiley, Chichester, West Sussex, U.K, 5th ed edition, 2011. OCLC: ocn666867900.
- Shailender Kumar. Study of time-varying data models.
- David J Hand, Gordon Blunt, Mark G Kelly, Niall M Adams, et al. Data mining for fun and profit. Statistical Science, 15(2):111–131, 2000.
- Eamonn Keogh, Selina Chu, David Hart, and Michael Pazzani. Segmenting time series: A survey and novel approach. In Data mining in time series databases, pages 1–21. World Scientific, 2004.
- Eamonn Keogh, Kaushik Chakrabarti, Michael Pazzani, and Sharad Mehrotra. Locally adaptive dimensionality reduction for indexing large time series databases. ACM Sigmod Record, 30(2):151–162, 2001.
- Enrique Castillo, Antonio J Conejo, Pablo Pedregal, Ricardo Garcia, and Natalia Alguacil. Building and solving mathematical programming models in engineering and science, volume 62. John Wiley & Sons, 2011.
- MAN Diesel and Turbo. Basic Principles of Ship Propulsion. MAN Diesel and Turbo, 2013.