000007612 001__ 7612
000007612 005__ 20240531164502.0
000007612 02470 $$2doi$$a10.24868/issn.2515-818X.2018.042
000007612 035__ $$a2449409
000007612 037__ $$aGENERAL
000007612 245__ $$aApplication of Machine Learning and Mathematical Programming in the Optimization of the Energy Management System for Hybrid-Electric Vessels Having Cyclic Operations
000007612 269__ $$a2018-10-03
000007612 336__ $$aConference Proceedings
000007612 520__ $$aShipping 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. 
000007612 542__ $$fCC-BY-NC-ND-4.0
000007612 6531_ $$aEnergy management system
000007612 6531_ $$ahybrid-electric vessel
000007612 6531_ $$acyclic operational profiles
000007612 6531_ $$aunsupervised machine learning algorithms
000007612 6531_ $$amathematical programming
000007612 6531_ $$aenergy efficiency
000007612 7001_ $$aMohammadzadeh, N$$uPolitecnico di Milano, Italy
000007612 7001_ $$aBaldi, F$$uEcole Polytechnique Federale de Lausanne (EPFL), Switzerland
000007612 7001_ $$aBoonen, E$$uDamen shipyard, the Netherlands
000007612 773__ $$tConference Proceedings of INEC
000007612 773__ $$jINEC 2018
000007612 789__ $$whttps://zenodo.org/record/2449409$$2URL$$eIsIdenticalTo
000007612 85641 $$uhttps://imarest.org/inec$$yConference website
000007612 8564_ $$9cbb550ba-ea79-41f8-b714-90b23ab9b317$$s2511688$$uhttps://library.imarest.org/record/7612/files/INEC%202018%20Paper%20064%20Mohammadzadeh%20SDG%20FINAL.pdf