Robust Energy Management for a Microgrid

Jens Hönen*, Johann L. Hurink, Bert Zwart

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Due to the increasing penetration of photovoltaic (PV) systems, electric vehicles (EV) and other smart devices on a household level, the role of consumers changes from pure consumption to production and storage of electricity. These prosumers will also directly participate in future electricity markets. To compensate for the small scale and the fluctuations in their demand and production, one promising approach for prosumers is to form small energy communities or microgrids, and participate in the electricity markets as one entity. A challenge for these microgrids is to find an optimal energy management strategy, mainly due to the uncertainty in electricity prices, in PV generation as Well as in the prosumer loads. To integrate this uncertainty into the planning, an adaptive robust optimization approach using linear decision rules is proposed in this paper. The linear decision rules allow for a delayed determination of some of the decisions and can therefore adapt to realizations of the uncertainty. Three different uncertainty scenarios are used to evaluate and compare the proposed approach in a case study and to get more structural insights into the efficiency of the approach.

Original languageEnglish
Title of host publicationENERGYCON 2022 - 2022 IEEE 7th International Energy Conference, Proceedings
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-6654-7982-0
DOIs
Publication statusPublished - 21 Jul 2022
Event7th IEEE International Energy Conference, ENERGYCON 2022 - Riga, Latvia
Duration: 9 May 202212 May 2022
Conference number: 7
http://ieee-energycon2022.org/

Conference

Conference7th IEEE International Energy Conference, ENERGYCON 2022
Abbreviated titleENERGYCON 2022
Country/TerritoryLatvia
CityRiga
Period9/05/2212/05/22
Internet address

Keywords

  • adaptive robust optimization
  • energy management
  • energy transition
  • microgrid
  • uncertainty

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