Model predictive control based energy management of a household microgrid

Pushed by sustainable policies, energy engagement and cost reduction, the installed photovaltaic (PV) capacity has a rapidly growth among householders. However, challenges that come with it are the management of excess PV and improvement of economic benefit. This paper modeled a grid-connected residential microgrid composed of PV, battery, water tank, cleaning pump, HVAC, lighting and other electricity usage. An advanced energy management strategy based on model predictive control (MPC) is proposed to redistribute power consumption, achieve minimization of energy cost and improve self-consumption ability through load shifting. A penalty coefficient that is large but decreasing with time can be added to the MPC cost function to improve self-consumption ratio if there is no feed-in tariff. Simulation results are given to verify the effectiveness of the proposed strategy. Finally, the microgrid platform has been established, which applied open platform communication (OPC) technology to ensure the real-time operation of MPC control. Experiment results demonstrate the validation and feasibility of the proposed MPC based strategies.