This thesis investigates the optimal scheduling of smart home appliances with respect to economic benefits (electricity bill) and reducing environmental impacts (CO2 emissions) for the Stockholm Royal Seaport project.
The aim of this project is to develop a new urban district developing in the eastern Stockholm which will house 10,000 new apartments and 30,000 new office spaces where modern living is combined with environmental thinking to create sustainable living.
In a previous work the scheduling objective was to minimize electricity bill, subject to various constraints such as sequential processing and consumer preferences. In this work the optimization framework will be extended to consider the trade-off between electricity bill and CO2 emission minimization.
This is a main concern in the Royal Seaport project. The study of this thesis shows that a well balanced result between minimizing the electricity cost and reducing the CO2 emissions for an unusual cold day in Sweden (2010-01-05) with three typical home appliances showed that for formulation suggested in a previous work one could save up to 35.9% of electricity costs as well as reducing the CO2 emissions with up to 16.5 %.
This saving is with respect to the worst case scheduling for that specific day. The trade-off analysis is based on multi-objective Pareto frontier exploration, which requires solving multiple schedules instead of one as in the previous single objective case.
In addition, for practical implementation the smart home control devices that will be used in the Stockholm Royal Sea project apartments will have a CPU and memory similar to those of a smart phone. Therefore there exists a need for faster implementation. The studies in this thesis indicate that the proposed simplified formulation can lead to a almost sixfold speed up in solve time, while providing schedules similar to those by the previous approach.
The solve time of the proposed formulation decreases when the number of breakpoints in the piecewise linear objective decreases. A variant of the Ramer-Douglas-Peucker algorithm is applied to reduce the number of breakpoints while guaranteeing the objective function error is within a pre-specified bound. Finally, extensions of the current framework are discussed.
Author: Wu, Jonas