Nonlinear Model Predictive Control for the Coordination of Electric Loads in Smart Homes

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.