RL1. Modeling of Energy Systems

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

Carmen Galán-Marín
Macarena Gómez Mármol
Salvador Pineda Morente
Carlos Rivera Gómez

Research portfolio:

Mathware for the operation of cyber-physical systems
  • Mathematical techniques:
    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
  • Application sectors:
    Energy, Smart grids, Portfolio optimization, vehicle routing
Mathware for the operation and planning of fully renewable energy systems
  • Mathematical techniques:
    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.
  • Application sectors:
    Energy, Smart grids, Power systems, District heating, Gas networks, Water management
Reduced Order Modelling of unsteady and turbulent flows
  • Mathematical techniques:
    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.
  • Application sectors:
    Modeling problems involving parametric turbulent flows: Renewable energy systems, energy-efficient buildings, automotive industry, aircraft industry, blood and other physiological flows.

Related projects:

Related transfer: