Model predictive control of consensus-based energy management system for DC microgrid

Ali, Syed Umaid; Waqar, Asad; Aamir, Muhammad; Qaisar, Saeed Mian; Iqbal, Jamshed

Abstract

The increasing deployment and exploitation of distributed renewable energy source (DRES) units and battery energy storage systems (BESS) in DC microgrids lead to a promising research field currently. Individual DRES and BESS controllers can operate as grid-forming (GFM) or grid-feeding (GFE) units independently, depending on the microgrid operational requirements. In standalone mode, at least one controller should operate as a GFM unit. In grid-connected mode, all the controllers may operate as GFE units. This article proposes a consensus-based energy management system based upon Model Predictive Control (MPC) for DRES and BESS individual controllers to operate in both configurations (GFM or GFE). Energy management system determines the mode of power flow based on the amount of generated power, load power, solar irradiance, wind speed, rated power of every DG, and state of charge (SOC) of BESS. Based on selection of power flow mode, the role of DRES and BESS individual controllers to operate as GFM or GFE units, is decided. MPC hybrid cost function with auto-tuning weighing factors will enable DRES and BESS converters to switch between GFM and GFE. In this paper, a single hybrid cost function has been proposed for both GFM and GFE. The performance of the proposed energy management system has been validated on an EU low voltage benchmark DC microgrid by MATLAB/ SIMULINK simulation and also compared with Proportional Integral (PI) & Sliding Mode Control (SMC) technique. It has been noted that as compared to PI & SMC, MPC technique exhibits settling time of less than 1μsec and 5% overshoot.

Citation

Ali, S. U., Waqar, A., Aamir, M., Qaisar, S. M., & Iqbal, J. (2023). Model predictive control of consensus-based energy management system for DC microgrid. PLoS ONE, 18(1), Article e0278110. https://doi.org/10.1371/journal.pone.0278110

• Journal Article Type: Article
• Acceptance Date: Nov 10, 2022
• Online Publication Date: Jan 20, 2023
• Publication Date: Jan 20, 2023
• Deposit Date: Nov 12, 2022
• Publicly Available Date: Jan 23, 2023
• Journal: PLoS ONE
• Print ISSN: 1932-6203
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 18
• Issue: 1
• Article Number: e0278110
• DOI: https://doi.org/10.1371/journal.pone.0278110
• Public URL: https://hull-repository.worktribe.com/output/4125205