A Robust MPC Energy Scheduling Strategy for Multi-Carrier Microgrids

Raffaele Carli, Graziana Cavone, Tomas Pippia, Bart De Schutter, Mariagrazia Dotoli

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

Abstract

We present a Robust Model Predictive Control (RMPC) approach for multi-carrier microgrids, i.e., microgrids based on gas and electricity. The microgrid that we consider includes thermal loads, electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power plants. Moreover, the system under control is affected by several external disturbances, e.g., uncertainty in renewable energy generation, electrical and thermal demand. The goal of the controller is to minimize the overall economical cost and the energy exchange with the main grid, while guaranteeing comfort. Whereas several RMPC methods have been developed for electrical or thermal microgrids, little or no attention has been devoted to robust control of multi-carrier microgrids. Therefore, we consider a novel RMPC algorithm that can improve the performance with respect to classical deterministic Model Predictive Control (Det-MPC) controllers in the context of multi-carrier microgrids. The RMPC method relies on the box-uncertainty-set robust optimization, where uncertain parameters are assumed to take their values from different intervals independently. The RMPC approach is able to successfully satisfy the constraints even in the presence of the mentioned disturbances. Simulations of a realistic residential case study show the benefits of the proposed approach with respect to Det-MPC controllers.

Original languageEnglish
Title of host publicationProceedings of the IEEE 16th International Conference on Automation Science and Engineering, CASE 2020
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages152-158
ISBN (Electronic)978-1-7281-6904-0
DOIs
Publication statusPublished - 2020
Event16th IEEE International Conference on Automation Science and Engineering, CASE 2020 - Hong Kong, Hong Kong
Duration: 20 Aug 202021 Aug 2020

Conference

Conference16th IEEE International Conference on Automation Science and Engineering, CASE 2020
CountryHong Kong
CityHong Kong
Period20/08/2021/08/20

Keywords

  • Energy and Environment-Aware Automation
  • Microgrid
  • Optimization and Optimal Control
  • Robust Model Predictive Control
  • Set-based Uncertainty

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