A multi-energy multi-microgrid system planning model for decarbonisation and decontamination of isolated systems
Distributionally Robust Models for Multi-Stage Stochastic Optimization Problems
This research project aims to develop new models, theory, and algorithms for dynamic optimization model problems that incorporate robustness for the underlying probability distributions. We focus our work on the important classes of multistage stochastic problems, in which information is revealed period by period and decisions are made accordingly, using information from previous stages.
Adapting to the uncertainties and risks of climate change: Advanced methods and models for energy systems and markets
The aim of the project is to develop new mathematical models and computational methods that help the private and public sector to adapt energy systems and markets to high uncertainties and risks derived from climate change. To achieve this goal, the research addresses three research objectives: (1) Model and classify the different types of uncertainties and risks associated with climate change; (2) Develop energy planning…
GEMA: Improving energy management in micro grids with storage via stochastic optimization and machine learning
The overall aim of this project is to improve and implement an optimal energy management system for generation systems based on photovoltaic panels and with storage units. These systems can be both on-grid (connected to the network) and off-grid (isolated systems, generally in remote locations), and connected to a small number of users, such as a home or a small business. Specifically, the management system…