Industrial Application of a Model Predictive Control Solution for Power Plant Startups

2006 ◽  
Author(s):  
F.J. D'Amato
Keyword(s):  
Power Plant ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Industrial Application ◽  
Control Solution

scholarly journals Sensitivity Analysis of the Battery Model for Model Predictive Control: Implementable to a Plug-In Hybrid Electric Vehicle

2018 ◽  
Vol 9 (4) ◽  
pp. 45 ◽  
Author(s):  
Nicolas Sockeel ◽  
Jian Shi ◽  
Masood Shahverdi ◽  
Michael Mazzola
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Predictive Control ◽  
Hybrid Electric Vehicle ◽  
Control Solution ◽  
Current Time ◽  
Battery Model ◽  
Hybrid Electric

Developing an efficient online predictive modeling system (PMS) is a major issue in the field of electrified vehicles as it can help reduce fuel consumption, greenhouse gasses (GHG) emission, but also the aging of power-train components, such as the battery. For this manuscript, a model predictive control (MPC) has been considered as PMS. This control design has been defined as an optimization problem that uses the projected system behaviors over a finite prediction horizon to determine the optimal control solution for the current time instant. In this manuscript, the MPC controller intents to diminish simultaneously the battery aging and the equivalent fuel consumption. The main contribution of this manuscript is to evaluate numerically the impacts of the vehicle battery model on the MPC optimal control solution when the plug hybrid electric vehicle (PHEV) is in the battery charge sustaining mode. Results show that the higher fidelity model improves the capability of accurately predicting the battery aging.

scholarly journals Improving Flexibility and Energy Efficiency of Post-Combustion CO2 Capture Plants Using Economic Model Predictive Control

Processes ◽  
2018 ◽  
Vol 6 (9) ◽  
pp. 135 ◽  
Author(s):  
Benjamin Decardi-Nelson ◽  
Su Liu ◽  
Jinfeng Liu
Keyword(s):  
Energy Efficiency ◽  
Power Plant ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Economic Performance ◽  
Economic Model ◽  
Power Plants ◽  
Gas Flow ◽  
Renewable Energy Sources ◽  
Economic Model Predictive Control

To reduce CO 2 emissions from power plants, electricity companies have diversified their generation sources. Fossil fuels, however, still remain an integral energy generation source as they are more reliable compared to the renewable energy sources. This diversification as well as changing electricity demand could hinder effective economical operation of an amine-based post-combustion CO 2 capture (PCC) plant attached to the power plant to reduce CO 2 emissions. This is as a result of large fluctuations in the flue gas flow rate and unavailability of steam from the power plant. To tackle this problem, efficient control algorithms are necessary. In this work, tracking and economic model predictive controllers are applied to a PCC plant and their economic performance is compared under different scenarios. The results show that economic model predictive control has a potential to improve the economic performance and energy efficiency of the amine-based PCC process up to 6% and 7%, respectively, over conventional model predictive control.

Model Predictive Control and the Optimization of Power Plant Load while Considering Lifetime Consumption

2001 ◽  
Vol 21 (11) ◽  
pp. 54-54 ◽  
Author(s):  
E. Gallestey ◽  
A. Stothert ◽  
M. Antoine ◽  
S. Morton
Keyword(s):  
Power Plant ◽  
Model Predictive Control ◽  
Predictive Control
Sign in / Sign up

Export Citation Format

Share Document