Control of Microgrids using an Enhanced Model Predictive Controller

Muhammed Cavus*, Adib Allahham, Kabita Adhikari, Mansoureh Zangiabadi, Damian Giaouris

*Corresponding author for this work

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

5 Citations (Scopus)


Renewable energy sources have been widely adopted to stop global warming. This growing adaptation has led to a significant change in topologies of traditional power networks, and now we have the concept of a microgrid. Model Predictive Control is an advanced method that is used to control power systems while satisfying several constraints to achieve an optimal solution based on various criteria. Although, Model Predictive Control is robust and has several advantages, its implementation is often very complex and requires high computational power. On the other hand, ε-variables based control strategies, which are practical methods to model control strategies in microgrids, are able to simplify the control structure allowing more scalability and even resilience. This paper presents, a hybrid method to simplify the implementation of Model Predictive Control using ε-variables and make it more effective on complicated energy systems. Our results demonstrate that combining Model Predictive Control with ε-variables can significantly simplify the control structure and hence allow for more complicated control strategies to be employed in order to provide extra benefits to the energy system like scalability and robustness.

Original languageEnglish
Title of host publication11th International Conference on Power Electronics, Machines and Drives (PEMD 2022)
Place of PublicationPiscataway, US
Number of pages6
ISBN (Electronic)9781839537189
Publication statusPublished - 10 Oct 2022
Externally publishedYes
Event11th International Conference on Power Electronics, Machines and Drives, PEMD 2022 - Newcastle, Virtual, United Kingdom
Duration: 21 Jun 202223 Jun 2022


Conference11th International Conference on Power Electronics, Machines and Drives, PEMD 2022
Country/TerritoryUnited Kingdom
CityNewcastle, Virtual

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