TY - GEN
T1 - Nonlinear model predictive control for the coordination of electric loads in smart homes
AU - Divecha, Avinash
AU - Stockar, Stephanie
AU - Rizzoni, Giorgio
N1 - Publisher Copyright:
Copyright © 2017 ASME.
PY - 2017
Y1 - 2017
N2 - Demand-response programs offer a viable solution for improving the grid efficiency and reliability though the shaping of the consumer's power demand. For the customers to fully benefit from varying electricity prices, an energy management strategy that coordinates the electrical loads is required. In this framework, this paper uses a Nonlinear Model Predictive Control (MPC) strategy to solve the coupled problem of optimally scheduling home appliances, Heating, Ventilation and Air Conditioning (HVAC) system and controlling electric vehicle charging. Simulation results are presented on selected case studies to demonstrate the ability of the Particle Swarm Optimization (PSO) to solve the optimization problem for a single home faster than real-Time. Results show that this strategy is always able to provide near-optimal solutions with limited computation time and no reconfiguration of the control scheme for applications to houses equipped with different technologies.
AB - Demand-response programs offer a viable solution for improving the grid efficiency and reliability though the shaping of the consumer's power demand. For the customers to fully benefit from varying electricity prices, an energy management strategy that coordinates the electrical loads is required. In this framework, this paper uses a Nonlinear Model Predictive Control (MPC) strategy to solve the coupled problem of optimally scheduling home appliances, Heating, Ventilation and Air Conditioning (HVAC) system and controlling electric vehicle charging. Simulation results are presented on selected case studies to demonstrate the ability of the Particle Swarm Optimization (PSO) to solve the optimization problem for a single home faster than real-Time. Results show that this strategy is always able to provide near-optimal solutions with limited computation time and no reconfiguration of the control scheme for applications to houses equipped with different technologies.
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U2 - 10.1115/DSCC2017-5366
DO - 10.1115/DSCC2017-5366
M3 - Conference contribution
AN - SCOPUS:85036612888
T3 - ASME 2017 Dynamic Systems and Control Conference, DSCC 2017
BT - Vibration in Mechanical Systems; Modeling and Validation; Dynamic Systems and Control Education; Vibrations and Control of Systems; Modeling and Estimation for Vehicle Safety and Integrity; Modeling and Control of IC Engines and Aftertreatment Systems;Unmanned Aerial Vehicles (UAVs) and Their Applications; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Control of Smart Buildings and Microgrids; Energy Systems
PB - American Society of Mechanical Engineers
T2 - ASME 2017 Dynamic Systems and Control Conference, DSCC 2017
Y2 - 11 October 2017 through 13 October 2017
ER -