Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control

N��ez, Alfredo; Mar�n, Luis Gabriel; S�ez, Doris; Sumner, Mark; Pholboon, Seksak; Mu�oz-Carpintero, Diego; K�brich, Daniel

Abstract

© 2019 by the authors. This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of the microgrid as well as the distribution network operator (DNO). The higher level of the EMS comprises a robust MPC controller which optimizes energy usage and defines a power reference that is tracked by the lower-level real-time controller. The proposed EMS addresses the uncertainty of the predictions of the generation and end-user consumption profiles with the use of the robust MPC controller, which considers the optimization over a control policy where the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. Simulation results using data from a real urban community showed that when compared with an equivalent (non-robust) deterministic EMS (i.e., an EMS based on the same MPC formulation, but without the uncertainty handling), the proposed EMS based on robust MPC achieved reduced energy costs and obtained a more uniform grid power consumption, safer battery operation, and reduced peak loads.

Citation

Núñez, A., Marín, L. G., Sáez, D., Sumner, M., Pholboon, S., Muñoz-Carpintero, D., & Köbrich, D. (2019). Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control. Energies, 12(23), Article 4453. https://doi.org/10.3390/en12234453

• Journal Article Type: Article
• Acceptance Date: Nov 20, 2019
• Online Publication Date: Nov 22, 2019
• Publication Date: Nov 22, 2019
• Deposit Date: Dec 4, 2019
• Publicly Available Date: Dec 4, 2019
• Journal: Energies
• Electronic ISSN: 1996-1073
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 23
• Article Number: 4453
• DOI: https://doi.org/10.3390/en12234453
• Keywords: General Computer Science
• Public URL: https://nottingham-repository.worktribe.com/output/3476958
• Publisher URL: https://www.mdpi.com/1996-1073/12/23/4453/htm