Group Leader: Juan Miguel Morales González
Description:
RL1 deals with the research on mathware for the analysis, simulation and optimization of renewable and sustainable energy systems: rollout of smart power grids and design of energy efficient buildings and solar heliostat fields. The implementation of these methods call for solving large-scale non-convex optimization problems and accurately simulating parametric turbulent buoyant flows.
This research deals with the development of strategies for the active participation of distributed energy sources in wholesale energy markets; building of tools for planning renewables-based energy systems; design of mechanisms for the integrated operation of different energy systems (gas, power and heat) and modelling of thermal performances of buildings, in view of its incorporation into official energy qualification of buildings codes.
Members:
Samuele Rubino
Juan Valverde García
Juan Valverde García
Carmen Galán-Marín
Macarena Gómez Mármol
Salvador Pineda Morente
Carlos Rivera Gómez
Research portfolio:
Mathware for the operation of cyber-physical systems
A. Optimization under uncertainty
B. Game theory and hierarchical optimization. Multilevel programming.
C. Distributed optimization and mathematical decomposition techniques.
D. Statistical and machine learning. Time series analysis and forecasting.
E. High-performance computing and simulation.
F. Theory of partial differential equations.
G. Metaheuristics
Energy, Smart grids, Portfolio optimization, vehicle routing
Mathware for the operation and planning of fully renewable energy systems
A. Optimization under uncertainty
B. Game theory and hierarchical optimization. Multilevel programming.
C. Distributed optimization and mathematical decomposition techniques.
D. Scenario generation and reduction.
E. High-performance computing and simulation.
F. Global optimization.
G. Combinatorial optimization.
Energy, Smart grids, Power systems, District heating, Gas networks, Water management
Reduced Order Modelling of unsteady and turbulent flows
A. A priori error estimation.
B. Approximation of regular branches of non-linear equations.
C. Reduced basis discretizations.
D. Mathematical analysis and approximation of turbulent flow models.
E. High performance computing.
Modeling problems involving parametric turbulent flows: Renewable energy systems, energy-efficient buildings, automotive industry, aircraft industry, blood and other physiological flows.
Related projects:
- Advanced Analytics to Empower the Small Flexible Consumers of Electricity
- Mathematical Methods for Data-driven Power Systems
- SAND & SmArt distributioN griD simulator
- Smart TSO-DSO interaction schemes, market architectures and ICT Solutions for the integration of ancillary services from demand side management and distributed generation
- Centre for IT-Intelligent Energy Systems in Cities
- Accurate Roms for Insdustrial Applications
- Modelización de Orden Reducido Orientada al Diseño Eco-Eficiente de Edificios
- ROM Optimization for Architecture and Design
- Modelización de Orden Reducido de componentes de sub-malla en Métodos de Multiescala Variacional
Related transfer:
- Machine Learning for a Renewable European Energy System
- Renewable energy trading in wholesale electricity markets
- Modeling of light pollution due to urban lighting networks by Machine Learning techniques
- Modeling of chlorination concentration in public fountains by Machine Learning techniques
- Modeling of the electric perturbations in urban lighting networks by Machine Learning techniques
- Modeling of the facial recognizing distance by Machine Learning techniques
