Nonlinear model predictive control for the coordination of electric loads in smart homes

Avinash Divecha, Stephanie Stockar, Giorgio Rizzoni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationVibration 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
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791858295
DOIs
StatePublished - Jan 1 2017
EventASME 2017 Dynamic Systems and Control Conference, DSCC 2017 - Tysons, United States
Duration: Oct 11 2017Oct 13 2017

Publication series

NameASME 2017 Dynamic Systems and Control Conference, DSCC 2017
Volume3

Other

OtherASME 2017 Dynamic Systems and Control Conference, DSCC 2017
CountryUnited States
CityTysons
Period10/11/1710/13/17

Fingerprint

Electric loads
Domestic appliances
Model predictive control
Energy management
Electric vehicles
Air conditioning
Particle swarm optimization (PSO)
Ventilation
Electricity
Scheduling
Heating

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Mechanical Engineering

Cite this

Divecha, A., Stockar, S., & Rizzoni, G. (2017). Nonlinear model predictive control for the coordination of electric loads in smart homes. In 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 (ASME 2017 Dynamic Systems and Control Conference, DSCC 2017; Vol. 3). American Society of Mechanical Engineers. https://doi.org/10.1115/DSCC2017-5366
Divecha, Avinash ; Stockar, Stephanie ; Rizzoni, Giorgio. / Nonlinear model predictive control for the coordination of electric loads in smart homes. 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. American Society of Mechanical Engineers, 2017. (ASME 2017 Dynamic Systems and Control Conference, DSCC 2017).
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Divecha, A, Stockar, S & Rizzoni, G 2017, Nonlinear model predictive control for the coordination of electric loads in smart homes. in 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. ASME 2017 Dynamic Systems and Control Conference, DSCC 2017, vol. 3, American Society of Mechanical Engineers, ASME 2017 Dynamic Systems and Control Conference, DSCC 2017, Tysons, United States, 10/11/17. https://doi.org/10.1115/DSCC2017-5366

Nonlinear model predictive control for the coordination of electric loads in smart homes. / Divecha, Avinash; Stockar, Stephanie; Rizzoni, Giorgio.

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. American Society of Mechanical Engineers, 2017. (ASME 2017 Dynamic Systems and Control Conference, DSCC 2017; Vol. 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Divecha A, Stockar S, Rizzoni G. Nonlinear model predictive control for the coordination of electric loads in smart homes. In 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. American Society of Mechanical Engineers. 2017. (ASME 2017 Dynamic Systems and Control Conference, DSCC 2017). https://doi.org/10.1115/DSCC2017-5366