Analysis of optimal energy management in smart homes using MPC
Advanced building management systems utilize future information, such
as electricity spot prices, weather forecasts, and predicted electric
loads and hot water consumption, to reduce the maximum electric power
consumption and energy cost. A model predictive controller (MPC) is
implemented for a household with one hour sample intervals, including
hot water usage, charging of an electric vehicle, and domestic
heating, but also an accumulator water tank to be used as an
additional thermal energy storage. Both the maximum total power used
in the house and the energy cost are included in the cost
function to evaluate how these properties are affected by different
system designs. The MPC solution is compared to the
global optimal solution using dynamic programming indicating comparable
performance. The robustness of the MPC is evaluated using a
prediction of the future household electric consumption in the
controller. Results also show that a significant part of the cost
reduction is achieved for as small prediction horizons as five hours.
Analysis shows that including an accumulator tank is useful
for reducing the total energy cost, while reducing the peak
power is mainly achieved by increasing the prediction horizon of
the MPC.
Christofer Sundström, Daniel Jung and Anders Blom
2016
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Last updated: 2021-11-10
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