Abstract

Many countries in the world have a variable power rate, which a consumer can exploit to reduce electricity cost for running the appliances accordingly. In this greedy consumption, power surge is possible during that low cost slots and may result in load shedding in many parts. A consumer can schedule his appliance in some other way to minimize the cost and prevent power overloading. Appliance scheduling for cost optimization only addresses a local problem. A holistic view of the problem where the cost of consumers is reduced and also the load on the suppliers can be managed is a better approach. In this thesis we focus on scheduling the different appliances of different consumers so that total load on power supplier does not exceed the maximum load capacity and subsequently the total cost of consumer is minimized. We consider that there is only one power supplier for a given area with varying cost per unit hour. The hourly data collected from the smart meters are fed into the scheduler and the scheduler equipped with a wireless sensor schedules the appliances so that the maximum number of appliances are scheduled keeping overloading problem as constraint. Optimization using both Integer Linear Programming is discussed in this thesis.

Authors and Affiliations