Deep learning-based embedded mixed-integer model predictive control

Due to the recent developments in the practical implementation of remotely controlled switches (RCSs) in the smart distribution system infrastructure, distribution system operators face operational challenges in the hourly reconfigurable environment. This paper develops a stochastic Model Predictive Control (MPC) framework for operational scheduling of distribution systems with dynamic and adaptive hourly reconfiguration. The effect of coordinated integration of energy storage systems and demand response programs through hourly reconfiguration on the total costs (including cost of total loss, switching cost, cost of bilateral contract with power generation owners and responsive loads, and cost of exchanging power with the wholesale market) is investigated. A novel Switching Index (SI) based on the RCS ages and critical points in the network along with the maximum number of switching actions is introduced. Due to nonlinear nature of the problem and several existing binary variables, it is basically considered as a Mixed Integer Non-Linear Programming (MINLP) problem, which is transformed into a Mixed Integer Linear Programming (MILP) problem. The satisfactory performance of the proposed model is demonstrated through its application on a modified IEEE 33-bus distribution system.

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Regenerative Vibration Control of Tall Buildings Using Model Predictive Control

Volume 1: Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications; Bio-Medical and Bio-Mechanical Systems; Biomedical Robots and Rehab; Bipeds and Locomotion; Control Design Methods for Adv. Powertrain Systems and Components; Control of Adv. Combustion Engines, Building Energy Systems, Mechanical Systems; Control, Monitoring, and Energy Harvesting of Vibratory Systems ◽

10.1115/dscc2013-3988 ◽

2013 ◽

Cited By ~ 1

Author(s):

Yilun Liu ◽

Lei Zuo ◽

Xiudong Tang

Keyword(s):

Model Predictive Control ◽

Vibration Control ◽

Predictive Control ◽

Tall Buildings ◽

Mixed Integer ◽

Optimal Controller ◽

Control Force ◽

Online Computation ◽

Integer Quadratic Programming ◽

Mixed Integer Quadratic Programming

The regenerative Tuned Mass Damper (TMD) can convert the vibration energy of the tall building into the electricity, by replacing the damping element with electromagnetic harvester. The energy harvesting circuit therein which can regulate the electricity and control the vibration will introduce some constraints when designing vibration controller. This paper designed the vibration controller based on Model Predictive Control (MPC). The control force constraints were taken into consideration before designing the controller. The building model with semi-active constraints due to the regenerative properties of the TMD is converted into a Mixed Logical Dynamical (MLD) system first. Then the optimal controller is designed by solving the Mixed Integer Quadratic Programming (MIQP) problem. The results were evaluated and compared to the ones using “clipped-optimal” controller with the same constraints. It is found that the MPC controller can provide the same or better vibration control Results depending on the predicted horizon. Besides, an explicit MPC is obtained to reduce the online computation effort.

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

Sustainability ◽

10.3390/su132413907 ◽

2021 ◽

Vol 13 (24) ◽

pp. 13907

Author(s):

Xin Wang ◽

Jason Atkin ◽

Najmeh Bazmohammadi ◽

Serhiy Bozhko ◽

Josep M. Guerrero

Keyword(s):

Energy Storage ◽

Model Predictive Control ◽

Predictive Control ◽

Evaluation Framework ◽

Operation Management ◽

System Level ◽

Mixed Integer ◽

Battery Life ◽

Management Problem ◽

Objective Functions

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.

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