Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 25 October 2021 / Approved: 25 October 2021 / Online: 25 October 2021 (15:43:38 CEST)

How to cite: Wang, X.; Atkin, J.; Bazmohammadi, N.; Bozhko, S.; Guerrero, A.J.M. Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control. Preprints 2021, 2021100365 (doi: 10.20944/preprints202110.0365.v1). Wang, X.; Atkin, J.; Bazmohammadi, N.; Bozhko, S.; Guerrero, A.J.M. Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control. Preprints 2021, 2021100365 (doi: 10.20944/preprints202110.0365.v1).

Abstract

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 robustness of the model. Besides, 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

Subject

ENGINEERING, Electrical & Electronic Engineering

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