Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

Xin Wang, Jason Atkin, Najmeh Bazmohammadi, Serhiy Bozhko and Josep M. Guerrero
Additional contact information
Xin Wang: Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Nottingham, Nottingham NG8 1BB, UK
Jason Atkin: Computational Optimisation and Learning Lab, School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
Najmeh Bazmohammadi: Centre for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg East, Denmark
Serhiy Bozhko: Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Nottingham, Nottingham NG8 1BB, UK
Josep M. Guerrero: Centre for Research on Microgrids (CROM), AAU Energy, Aalborg University, 9220 Aalborg East, Denmark

Sustainability, 2021, vol. 13, issue 24, 1-24

Abstract: Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the feasibility of the model. In addition, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.

Keywords: model predictive control; mixed-integer linear programming; multi-objective optimization; energy storage management; load management; more electric aircraft; demand-side flexibility (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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