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.
- Dynamic Systems and Control Division
Nonlinear Model Predictive Control for the Coordination of Electric Loads in Smart Homes
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Divecha, A, Stockar, S, & Rizzoni, G. "Nonlinear Model Predictive Control for the Coordination of Electric Loads in Smart Homes." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 3: 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. Tysons, Virginia, USA. October 11–13, 2017. V003T42A005. ASME. https://doi.org/10.1115/DSCC2017-5366
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