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The three finalists of the 2016 EURO Doctoral Dissertation Award are:
Ruth Dominguez, Electrical Engineering, Universidad de Castilla - La Mancha
Planning and Operations in Fully Renewable Electric Energy Systems
The integration of renewable energies in power systems represents a key point to achieve a sustainable development, since more than 30% of the total GHG emissions are due to electricity generation. However, the electricity production from wind and solar technologies depends on resources which are variable and, in general, difficult to predict. Hence, high penetration of stochastic resources in power systems introduces high levels of uncertainty in planning and operations. On the other hand, renewable technologies are still under maturing stage and their investment costs are subject to high uncertainty. Therefore, in this dissertation we focus on the operation and planning of renewable-dominated power systems. A scheduling model for energy and reserves in the day-ahead market considering a fully renewable electric energy system where most of the electricity is supplied by non-dispatchable technologies is proposed. Additionally, static and multi-stage investment models are proposed to efficiently integrate renewable energies in power systems. We use stochastic programming to model the uncertainty involved in such models. A linear-decision-rule approach is also applied to efficiently solve multi-stage investment models. Finally, numerous realistic case studies are analyzed.
Theoretical and Practical Contributions on Scatter Search, Variable Neighbourhood Search and Matheuristics for 0-1 Mixed Integer Programs
This work consists in results obtained studying Scatter Search (SS), Variable Neighbourhood Search (VNS), and Matheuristics in both theoretical and practical contexts. The main theoretical contribution of this work is a convergent SS algorithm for 0-1 Mixed Integer Programs (MIP) along with the proof of its finite convergence. Additionally, we identify two variants of the implementation of a convergent SS algorithm. Stemming from this convergent SS algorithm several SS heuristics have been proposed and tested on some instances of 0-1 MIP. Our findings demonstrate the efficacy of these first stage methods, which makes them attractive for use in situations where very high quality solutions are sought with an efficient investment of computational effort. This work also includes new variants of VNS metaheuristic that have been successfully applied for solving NP-Hard problems arising in transportation, logistics, power generation, scheduling and clustering. On all tested problems, the proposed VNS heuristics turn out to be new state-of-the art heuristics. The last contribution of this work consists in proposing several matheuristics for solving the Fixed-Charge Multicommodity Network Design problem and finding a first feasible 0-1 MIP solution. The performances of these methods have been disclosed on the benchmark instances and the obtained results demonstrate the competitiveness of the proposed methods with other approaches in the literature.
Reactive Robustness and Integrated Approaches for Railway Optimization Problems
Planning railway operations is not a simple task as it entails solving multiple interdependent optimization problems. My thesis focuses on recovery methods and the integration of interdependent planning problem. During recovery fast solution methods are essential, and solving problems in isolation can be problematic as it can lead to an overall infeasibility. Several railway problems are studied in this thesis, and both theoretical and practical contributions have been made. The problems have been formulated as optimization problems and solution methods have been proposed to solve them using optimization theory and techniques. Real-life and realistic data has been used to benchmark and test the solution methods. The central actor of the thesis is the rolling stock running on the railway infrastructure. With a given public timetable a rolling stock schedule is sought that provides the best compromise between operational cost, robustness, contract requirements and passenger satisfaction. In between train services the rolling stock units must be parked in the available depots avoiding conflicting movements. Furthermore, rolling stock units are heavy and consume a considerable amount of energy in operation; with proper optimization tools a significant amount of the energy can be saved. A prompt optimization of individual train journeys helps the driver to drive efficiently and enhances robustness in a realistic (dynamic) environment.