A parametrized Model Predictive Control approach for microgrids

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

6 Citations (Scopus)

Abstract

We propose a parametrized Model Predictive Control (MPC) approach for optimal operation of microgrids. The parametrization expresses the control input as a function of the states, variables, and parameters. In this way, it is possible to apply an MPC approach by optimizing only the parameters and not the inputs. Moreover, the value of the binary control variables in the model is assigned according to parametrized heuristic rules, thus obtaining a formulation for the optimization problem that is more scalable compared to standard approaches in the literature. Furthermore, we propose a control scheme based on one single controller that uses two different sampling times and prediction models. By doing so, we can include both fast and slow dynamics of the system at the same level. This control approach is applied to an operational control problem of a microgrid, which includes local loads, local production units, and local energy storage systems and results show the effectiveness of the proposed appro.
Original languageEnglish
Title of host publicationProceedings of the 57th IEEE Conference on Decision and Control (CDC 2018)
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages3171-3176
ISBN (Print)978-1-5386-1395-5
DOIs
Publication statusPublished - 2018
EventCDC 2018: 57th IEEE Conference on Decision and Control - Miami, United States
Duration: 17 Dec 201819 Dec 2018

Conference

ConferenceCDC 2018: 57th IEEE Conference on Decision and Control
CountryUnited States
CityMiami
Period17/12/1819/12/18

Keywords

  • Microgrids
  • Batteries
  • Generators
  • Supercapacitors
  • Predictive models
  • Mathematical model
  • Computational modeling

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