Type: Semester project (10 credits) / Master project (30 credits)
Period: 2018 Spring (will be offered also in 2018 Fall)
Despite its low media coverage, shipping is one of the most important industrial sectors of today’s society. Roughly 90% of international trade is transported by sea, and the sector has seen one of the most impressive growth rates across all industries. Of all the different types of actors in the field, cruise companies have shown the highest growth rate in the latest twenty years.
However, the issue of climate change and the associated reactions on a global scale are starting to affect the shipping industry. Despite being recognized as the most energy efficient way to transport goods around the world, ships contribute to a significant part of human anthropic greenhouse gas emission, and have shown a large potential for improvement. Ships are demanded to become more energy efficient in the future, and these restrictions are only expected to become tougher in the future.
Process integration and energy systems optimization techniques can contribute to finding a solution to this problem. In addition to developing new and more efficient technologies to be used in ship energy systems, their optimal choice, sizing and integration needs to be addressed thoroughly as system design, problems, in order to avoid wrong decisions or sub-optimal choices.
One of the main challenges in the field of energy system optimization is the uncertainty of a number of input parameters to the optimization. This is particularly true for cost parameters, such the expected cost for the installation of energy efficient technologies (batteries, waste heat recovery systems, heat pumps, etc.) and the expected cost of the fuel over the lifetime of the investment.
Robust optimization is one of possible solutions to this challenge. In robust optimization, uncertain parameters are given uncertainty bounds, and the optimal solution is calculated ensuring protection against worst-case scenarios. Robust optimization allows decreasing the risk for unsuccessful projects, providing solutions that offer a higher guarantee of meeting performance targets at the price of marginally higher costs. This makes robust optimization particularly suitable for the optimization of energy systems in general, and particularly for ship energy systems.
- Learning about optimization and familiarizing with the statement and coding of an optimization problem.
- Classifying and evaluating uncertainty within an energy system design problem
- Application of robust optimization to a case study
- Get accustomed with the main concepts and areas of research touched within the project. In particular
- Ship energy systems modelling and optimization
- Sensitivity analysis
- Robust optimization
- Get accustomed to the different computer softwares developed at the IPESE lab, and that will be the fundamental tools to be used in the thesis work. This refers particularly to the optimization software Osmose.
- Understand the models for ship energy systems components currently available in the Osmose model library. In case it is deemed necessary, write additional models.
- Perform a global sensitivity analysis of the ship energy systems optimization problem. This activity should include the analysis of the sensitivity to both uncertain parameters and to nonlinear decision variables.
- Select the most influential uncertain variables to include in the robust optimization
- Perform the robust optimization of the system. In particular, the concept of targetoriented robust optimization will be used. According to this particular form of robust optimization, the system will be optimized in its energy efficiency (instead for its economic performance) but we will implement robust constraints on the expected payback time of the system in order to prevent worst realizations (e.g. payback time becomes too high if fuel prices are too low).
Desired skills are:
- Energy conversion systems knowledge (EPFL courses: Thermo I/II, Energy conversion, Advanced Energetics, or equivalent);
- Programming skills: MATLAB, optimization (Modeling and Optimisation of Energy Systems EPFL course or equivalent)
- Statistics and optimization (linear programming
- Previous knowledge of ship systems can be a plus, but is not necessary.
If interested, please take contact with Stefano Moret and Francesco Baldi attaching your CV and transcript of records (Bachelor’s and Master’s). Ideally, the candidate will continue the work over a longer period of time, possibly another semester project and/or a Master thesis.
As the IPESE research group is now located in Sion, students might need to commute. All travel expenses will be covered by the IPESE lab budget.
Soroudi, A. and Amraee, T. (2013). Decision making under uncertainty in energy systems: State of the art. Renewable and Sustainable Energy Reviews, 28:376–384.
Lee, S. C. (2013). A target-oriented robust optimization approach to production and logistics planning systems. PhD thesis, National University of Singapore
Moret, S. (2017). Strategic energy planning under uncertainty. PhD Thesis, École Polytechnique Fédérale de Lausanne
Baldi, F., Maréchal, F. and Tammi, K. (2017). Process integration as a tool for the improvement of cruise ships energy efficiency. Proceedings of the Shipping in Changing Climate Conference. London, UK.
Baldi, F. (2016). Modelling, analysis and optimization of ship energy systems. PhD thesis, Chalmers University of Technology