Power Demand Side Management Using Particle Swarm Optimization in Smart Grid Community
Date Issued
2014
Date
2014
Author(s)
Lee, Shu-Fan
Abstract
The issues of energy conservation and carbon reduction have been discussed for years. Because the world evolves rapidly, conventional power grid suffers from the increasingly high power demand. For the next generation power grid, a.k.a. Smart Grid, being able to perform Demand Side Management (DMS) is crucial, i.e. capable of managing the energy demand at residences. With both DSM and Smart Grid, the residents not only can minimize their electricity cost but also can alleviate Peak-to-Average Ratio (PAR) of their total power consumption distribution. Here, minimization of electricity cost is achieved through optimal scheduling of daily activities, which simultaneously takes into account the distributed generation from Renewable Energy (RE) sources, the energy storage devices, and the dynamic pricing. In addition, an optimal PAR that makes the power system stabler can be achieved via cooperation among multiple homes in a community. However, most of the prior works focus on exchanges of appliance-level information but ignore the potential of context-awareness, e.g. disregarding the relation between activity and associated power consumption.
There are three major contributions in this thesis. Firstly, the context related to an activity is considered while optimization of the power demand is being performed. Secondly, we formulate the power demand side management into an optimization problem. For individual home power, the electricity cost is minimized. For multiple homes, electricity cost and PAR are minimized simultaneously without compromising the preference of residents. Thirdly, a method for scheduling daily activity is proposed for both individual home and the environment with multiple homes, i.e. community.
Subjects
智慧電網
需求端管理
再生能源
電力負載波動
SDGs
Type
thesis
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