Identification of realistic scheduling opportunities and base loads in smart homes using dynamic pricing and standard deviation of load profiles

Abstract

Oftentimes it is a challenge to be able to make an informed identification and differentiation of schedulable loads from non-schedulable loads in a household for adequate application of demand response programs. In most cases, non-schedulable loads, also referred to as essential or base loads, are hand-picked and chosen depending on the users' perception and conviction about what these loads could be. In this paper, we proposed a methodical and intelligent approach towards making this identification by considering the factors associated with the use of each appliance and as related to the household's load profile. These factors include the application of statistical methods based on the standard deviation of user's load profiles, as well as dynamic pricing factor. The technique applied involves the use of thresholds set by the user to determine the cut-off regions within the standard deviation of the load profile as well as on the day-ahead price data, from where information that will help to identify and distinguish schedulable from non-schedulable loads could be obtained. Results showed that a particular appliance which behaves as a schedulable load at certain times, could become a non-schedulable load at other times even within the same day. It therefore implies that any appliance can act as a schedulable or a non-schedulable load depending on factors associated with its use. It also suggests that this terminology remains relative, depending on the time and day of use as well as the type of service such loads offer the consumer.