4 research outputs found
Application of Robust Model Predictive Control to a Renewable Hydrogen-based Microgrid
In order to cope with uncertainties present in the renewable energy generation, as well as in the demand consumer, we propose in this paper the formulation and comparison of three robust model predictive control techniques, i. i. e., multi-scenario, tree-based, and chance-constrained model predictive control, which are applied to a nonlinear plant-replacement model that corresponds to a real laboratory-scale plant located in the facilities of the University of Seville. Results show the effectiveness of these three techniques considering the stochastic nature, proper of these systems
Methodology for energy management strategies design based on predictive control techniques for smart grids
This article focuses on the development of a general energy management system (EMS) design methodology
using on model-based predictive control (MPC) for the control and management of microgrids. Different MPCbased
EMS for microgrids have been defined in the literature; however, there is a lack of generality in the
proposed that would facilitate adapting to new architectures, energy storage system technology, nature of the
bus, application, or purpose. To fill this gap, a novel general formulation that is parameterizable, simple, easily
interpretable, and reproducible in different microgrid architectures is presented. This is the result of the
development of a novel methodology, which is also presented. It considers the state space formulation of the
controller from the initial modelling phase, from the dynamics of the energy storage systems represented by their
models to the subsequent definition of the optimisation problem. This is developed through the design of the
general cost function and the formulation of constrains, by means of general guidelines and reference values. To
evaluate the performance of the developed methodology, simulation tests were carried out for four different
microgrid architectures, with different applications and objectives, also considering different generation conditions,
demand profiles, and initial conditions. The results showed that, with some simple guidelines and
regardless of the case study, the developed MPC controller design methodology can address the technicaleconomic
optimisation problem associated with energy management in microgrids in an easy and intuitive way.This work was supported in part by grant PID2020-116616RB-C31
and grant PID2021-124908NB-I00 founded by MCIN/AEI/10.13039/
501100011033 and by “ERDF A way of making Europe”; by the Generalitat
Valenciana regional government through project CIAICO/2021/
064, by Andalusian Regional Program of R + D + i (P20- 00730), and by
the project “The green hydrogen vector. Residential and mobility
application”, approved in the call for research projects of the Cepsa
Foundation Chair of the University of Huelva
Application of robust model predictive control to a renewable hydrogen-based microgrid
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In order to cope with uncertainties present in the renewable energy generation, as well as in the demand consumer, we propose in this paper the formulation and comparison of three robust model predictive control techniques, i.e., multi-scenario, tree-based, and chance-constrained model predictive control, which are applied to a nonlinear plant-replacement model that corresponds to a real laboratory-scale plant located in the facilities of the University of Seville. Results show the effectiveness of these three techniques considering the stochastic nature, proper of these systems.Peer Reviewe