scholarly journals Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response

2018 ◽  
Vol 8 (3) ◽  
pp. 408 ◽  
Author(s):  
Radu Godina ◽  
Eduardo Rodrigues ◽  
Edris Pouresmaeil ◽  
João Matias ◽  
João Catalão
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Demand Response ◽  
Optimization Strategy ◽  
Home Energy Management

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.

scholarly journals Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids

2019 ◽  
Vol 11 (19) ◽  
pp. 5436 ◽  
Author(s):  
Galvan ◽  
Mandal ◽  
Chakraborty ◽  
Senjyu
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Management System ◽  
Peak Load ◽  
Test System ◽  
Energy Management System ◽  
Home Energy Management ◽  
The Future ◽  
A Charge

With the development of distributed energy resources (DERs) and advancements in technology, microgrids (MGs) appear primed to become an even more integral part of the future distribution grid. In order to transition to the smart grid of the future, MGs must be properly managed and controlled. This paper proposes a microgrid energy management system (MGEMS) based on a hybrid control algorithm that combines Transactive Control (TC) and Model Predictive Control (MPC) for an efficient management of DERs in prosumer-centric networked MGs. A locally installed home energy management system (HEMS) determines a charge schedule for the battery electric vehicle (BEV) and a charge–discharge schedule for the solar photovoltaic (PV) and battery energy storage system (BESS) to reduce residential customers’ operation cost and to improve their overall savings. The proposed networked MGEMS strategy was implemented in IEEE 33-bus test system and evaluated under different BEV and PV-BESS penetration scenarios to study the potential impact that large amounts of BEV and PV-BESS systems can have on the distribution system and how different pricing mechanisms can mitigate these impacts. Test results indicate that our proposed MGEMS strategy shows potential to reduce peak load and power losses as well as to enhance customers’ savings.

Sign in / Sign up

Export Citation Format

Share Document