scholarly journals Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Energies ◽  
2018 ◽  
Vol 11 (3) ◽  
pp. 631 ◽  
Author(s):  
Gianluca Serale ◽  
Massimo Fiorentini ◽  
Alfonso Capozzoli ◽  
Daniele Bernardini ◽  
Alberto Bemporad
Keyword(s):  
Energy Efficiency ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Problem Formulation ◽  
Hvac System ◽  
System Energy

On-Line Model Predictive Control for Energy Efficiency in Data Center

2021 ◽  
Vol 38 (12) ◽  
pp. 943-951
Author(s):  
Min Sik Chu ◽  
Hyun Ah Kim ◽  
Kyu Jong Lee ◽  
Ji Hoon Kang
Keyword(s):  
Energy Efficiency ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Data Center ◽  
Line Model ◽  
On Line

Electric Vehicle Battery Thermal and Cabin Climate Management Based on Model Predictive Control

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Energy Efficiency ◽  
Model Predictive Control ◽  
Electric Vehicle ◽  
Energy Management ◽  
Predictive Control ◽  
Critical Role ◽  
Peak Load ◽  
Energy Management Strategy ◽  
Battery Thermal Management System ◽  
Electric Vehicle Battery

Abstract Energy management plays a critical role in electric vehicle (EV) operations. To improve EV energy efficiency, this paper proposes an effective model predictive control (MPC)-based energy management strategy to simultaneously control the battery thermal management system (BTMS) and the cabin air conditioning (AC) system. We aim to improve the overall energy efficiency and battery cycle-life, while retaining soft constraints from both BTMS and AC systems. The MPC-based strategy is implemented by optimizing the battery operations and discharging schedules to avoid a peak load and by directly utilizing the regenerative power instead of recharging the battery. Compared with the benchmark system without any control coordination between BTMS and AC, the proposed MPC-based energy management has shown a 4.3% reduction in the recharging energy and a 6.5% improvement for the overall energy consumption. Overall, the MPC-based energy management is a promising solution to enhance the battery efficiency for EVs.

Model Predictive Control of Micro-CSP Integrated Into a Building HVAC System for Load Following Demand Response Programs

2019 ◽  
Author(s):  
Mohamed Toub ◽  
Mahdi Shahbakhti ◽  
Rush D. Robinett ◽  
Ghassane Aniba
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Demand Response ◽  
Dynamic Pricing ◽  
Power Flow ◽  
Voltage Regulation ◽  
Ancillary Services ◽  
Hvac System ◽  
Time Model ◽  
Load Following

Abstract Building heat, ventilation and air conditioning (HVAC) systems are good candidates for demand response (DR) programs as they can flexibly alter their consumption to provide ancillary services to the grid and contribute to frequency and voltage regulation. One of the major ancillary services is the load following demand response (DR) program where the demand side tries to track a DR load profile required by the grid. This paper presents a real-time Model Predictive Control (MPC) framework for optimal operations of a micro-scale concentrated solar power (MicroCSP) system integrated into an office building HVAC system providing ancillary services to the grid. To decrease the energy cost of the building, the designed MPC exploits, along with the flexibility of the building’s HVAC system, the dispatching capabilities of the MicroCSP with thermal energy storage (TES) in order to control the power flow in the building and respond to the DR incentives sent by the grid. The results show the effect of incentives in the building participation to the load following DR program in the presence of a MicroCSP system and to what extent this participation is affected by seasonal weather variations and dynamic pricing.

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.

scholarly journals Distributed Model Predictive Control applied to a VAV based HVAC system based on Sensitivity Analysis

2018 ◽  
Vol 51 (20) ◽  
pp. 259-264 ◽  
Author(s):  
Tejaswinee Darure ◽  
Vicenç Puig ◽  
Joseph Yamé ◽  
Frédéric Hamelin ◽  
Ye Wang
Keyword(s):  
Sensitivity Analysis ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Distributed Model ◽  
Hvac System ◽  
Distributed Model Predictive Control
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