TY - JOUR
T1 - Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control
AU - Wang, Xin
AU - Atkin, Jason
AU - Bazmohammadi, Najmeh
AU - Bozhko, Serhiy
AU - Guerrero, Josep M.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Demand-side flexibility
KW - Energy storage management
KW - Load management
KW - Mixed-integer linear programming
KW - Model predictive control
KW - More electric aircraft
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85121853852&partnerID=8YFLogxK
U2 - 10.3390/su132413907
DO - 10.3390/su132413907
M3 - Journal article
SN - 2071-1050
VL - 13
JO - Sustainability (Switzerland)
JF - Sustainability (Switzerland)
IS - 24
M1 - 13907
ER -