Model predictive control based home energy management system in smart grid

2015 ◽  
Author(s):  
Omar Alrumayh ◽  
Kankar Bhattacharya
Keyword(s):  
Smart Grid ◽  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Management System ◽  
Energy Management System ◽  
Home Energy Management ◽  
Home Energy Management System

scholarly journals Phase Balancing Home Energy Management System Using Model Predictive Control

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3323 ◽  
Author(s):  
Bharath Rao ◽  
Friederich Kupzog ◽  
Martin Kozek
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Management System ◽  
Mixed Integer ◽  
Energy Management System ◽  
Integer Quadratic Programming ◽  
Three Phase ◽  
Home Energy Management ◽  
Home Energy Management System

Most typical distribution networks are unbalanced due to unequal loading on each of the three phases and untransposed lines. In this paper, models and methods which can handle three-phase unbalanced scenarios are developed. The authors present a novel three-phase home energy management system to control both active and reactive power to provide per-phase optimization. Simplified single-phase algorithms are not sufficient to capture all the complexities a three-phase unbalance system poses. Distributed generators such as photo-voltaic systems, wind generators, and loads such as household electric and thermal demand connected to these networks directly depend on external factors such as weather, ambient temperature, and irradiation. They are also time dependent, containing daily, weekly, and seasonal cycles. Economic and phase-balanced operation of such generators and loads is very important to improve energy efficiency and maximize benefit while respecting consumer needs. Since homes and buildings are expected to consume a large share of electrical energy of a country, they are the ideal candidate to help solve these issues. The method developed will include typical distributed generation, loads, and various smart home models which were constructed using realistic models representing typical homes in Austria. A control scheme is provided which uses model predictive control with multi-objective mixed-integer quadratic programming to maximize self-consumption, user comfort and grid support.

scholarly journals Optimal Scheduling of Home Energy Management System With Plug-In Electric Vehicles Using Model Predictive Control

2018 ◽  
Author(s):  
Yue Zhao ◽  
Yan Chen ◽  
Brian Keel
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Electric Vehicles ◽  
Management System ◽  
Mixed Integer ◽  
Energy Management System ◽  
Electricity Cost ◽  
Home Energy Management ◽  
Home Energy Management System

For a household microgrid with renewable photovoltaic (PV) panel and plug-in electric vehicles (PEVs), a home energy management system (HEMS) using model predictive control (MPC) is designed to achieve optimal PEV charging and energy flow scheduling. Soft electric loads and an energy storage system (ESS) are also considered in the optimization of PEV charging in the MPC framework. The MPC is solved through a mixed-integer linear programming (MILP) by considering the relationship of energy flows in the optimization problem. Through the simulation results, the performance of optimization results under various electricity price plans is evaluated. The influences of PV capacities on the optimization results of electricity cost are also discussed.

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