scholarly journals Predictive Control for Energy Management in Ship Power Systems Under High-Power Ramp Rate Loads

2017 ◽  
Vol 32 (2) ◽  
pp. 788-797 ◽  
Author(s):  
Tuyen Van Vu ◽  
David Gonsoulin ◽  
Fernand Diaz ◽  
Chris S. Edrington ◽  
Touria El-Mezyani
Keyword(s):  
Power Systems ◽  
Energy Management ◽  
Predictive Control ◽  
High Power ◽  
Ramp Rate

Advanced chance-constrained predictive control for the efficient energy management of renewable power systems

2019 ◽  
Vol 74 ◽  
pp. 120-132 ◽  
Author(s):  
José D. Vergara-Dietrich ◽  
Marcelo M. Morato ◽  
Paulo R.C. Mendes ◽  
Alex A. Cani ◽  
Julio E. Normey-Rico ◽  
...  
Keyword(s):  
Power Systems ◽  
Energy Management ◽  
Predictive Control ◽  
Efficient Energy ◽  
Renewable Power

Energy Management of a PV-SB HPGS Based on Model Predictive Control

2020 ◽  
Author(s):  
Mostafa Algabalawy ◽  
Nesreen M. Samir
Keyword(s):  
Model Predictive Control ◽  
Power Systems ◽  
Energy Management ◽  
Predictive Control ◽  
Energy Demand ◽  
Economic Benefits ◽  
Pv Panel ◽  
Generation Costs ◽  
Potential Methods ◽  
Power Generation Systems

Abstract Background Hybrid Power Generation Systems (HPGSs) always introduces the solutions for many problems of the power systems such as; poor power quality and the highly generation costs. Usually, these systems have high opportunity of utilizing the renewable sources, where they are installed so near to the customers. In some times, the customers whom have installed these systems, have the ability to purchase or sell the required or the surplus energy, respectively. Therefore, the concept of the energy management should be implemented in these situations, which means providing energy to the consumers while respecting the demand and optimizing the production cost. Supplying power has become more and more complex in a growing network. Several ways exist to adapt to the steadily increasing energy demand. The energy efficiency of the buildings/generators could be improved, or new electric supply units could be built. However, smarter techniques of managing the grid from the demand side receive an increasing attention by research and industry for a promising economic potential. Methods The proposed HPGS in this paper is the combination of the Photovoltaic (PV) and the Storage Battery (SB). The Demand Response (DR) is the main philosophy for the energy management for PV-SB HPGS, while the Model Predictive Control (MPC) is applied to control the energy from the HPGS to the smart home, which was incorporated with an electrical heater in each room. The DR strategy has also been applied using other control techniques, such as thermostat and Proportional-Integral (PI) controller, for the purpose of comparing between the techniques. Results Simulation results for the three controllers were implemented. A comparison between the results was carried out. to verify the proposed system, which proved the superiority of the MPC. Conclusions A smart method for optimizing the comfort temperature inside the rooms by using a MPC strategy with a PV panel and a battery was proposed. DR allowed benefits on both system operation and market efficiency. MPC evidenced to provide economic benefits with respect to other compared controllers.

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