Automated Power Consumption Scheduling for Connected Appliances in a Remodeled, Energy Efficient House
Within our electric grid, focus should shift from maintaining a large system capacity buffer to establishing a usage-time buffer. This shift could reduce peaks in electrical demand, mitigate system variability, and increase utilization of lower cost and environmentally friendly energy sources. Methods to accomplish usage shifts fall under the label of demand-side management. One proposed method is that of automatically scheduling the usage of end-user appliances. While several promising results have been obtained, the optimization of appliance power usage based on data and detailed power profiles is relatively unexplored. Real appliance loads present opportunities and limitations for scheduling that have been ignored in prior approaches. Here, we examine energy consumption data collected from a retrofitted, energy efficient home. The appliances and systems in place are described with detailed power profiles taken from real operations. Using the established profiles, a real-time, multi-objective optimization is implemented with varying levels of prescient knowledge to schedule what would have been the ideal consumption profile for each day of usage at the home. Finally, these new consumption profiles are compared to the actual usage data to investigate the potential benefits afforded by automatic scheduling with connected appliances. Scheduling results show significant potential benefits for the end-user in terms of cost and peak consumption reduction regardless of the level of prescient knowledge. Appliance schedule interruptions are less valuable as the quality of predictions improve. Consideration for end-user comfort while scheduling could ease the transition to automated scheduling at the price of decreased cost and PAR reductions.
Aliprantis, Purdue University.
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