QoS-Driven Power Management of Data Centers via Model Predictive Control

2016 ◽  
Vol 13 (4) ◽  
pp. 1557-1566 ◽  
Author(s):  
Qiu Fang ◽  
Jun Wang ◽  
Qi Gong
Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2915
Author(s):  
Xiuyan Peng ◽  
Bo Wang ◽  
Lanyong Zhang ◽  
Peng Su

Shipboard integrated power systems, the key technology of ship electrification, call for effective failure mode power management control strategy to achieve the safe and reliable operation in dynamic reconfiguration. Considering switch reconfiguration with system dynamics and power balance restoration after reconfiguration, in this paper, the optimization objective function of optimal management for ship failure mode is established as a hybrid model predictive control problem from the perspective of hybrid system. To meet the needs for fast computation, a hierarchical hybrid model predictive control algorithm is proposed, which divides the original optimization problem into two stages, and reduces the computation complexity by relaxing constraints and the minimum principle. By applying to a real-time simulator in two scenarios, the results verify the effectivity of the proposed method.


2015 ◽  
Vol 23 (6) ◽  
pp. 2129-2143 ◽  
Author(s):  
Hyeongjun Park ◽  
Jing Sun ◽  
Steven Pekarek ◽  
Philip Stone ◽  
Daniel Opila ◽  
...  

Author(s):  
Ahmed M. Ali ◽  
Dirk Söffker

Abstract Power management in all-electric powertrains has a significant potential to optimally handle the limited energy and power density of electric power sources. Situation-based power management strategies (SB-PMSs), defining optimized solutions related to specific vehicle situations, offer the ability to reduce computational requirements and enhance the solution optimality of simple rule-based algorithms. Moreover, the local optimality of SB-PMSs can be addressed by considering online optimization of the situated solutions for limited horizons. This paper presents a novel PMSs using model predictive control (MPC) to define optimal control strategies based on situated solutions for fuel cell hybrid vehicles. Vehicle states are defined in terms of multiple characteristic variables and power management decisions are optimized offline for each vehicle states. Prediction of vehicle states is conducted using statistical predictive model based on state transitions in a number of driving cycles. Preoptimized solutions related to predicted states are iterated online to achieve better optimality over the look-ahead horizon. Results analysis from online testing revealed the ability of SB-MPC to improve the optimality of situation-based solutions and hence reduce total energy cost in different driving cycles.


2011 ◽  
Vol 44 (1) ◽  
pp. 10505-10510 ◽  
Author(s):  
Luca Parolini ◽  
Bruno Sinopoli ◽  
Bruce H. Krogh

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