Improved peak load management control technique for nonlinear and dynamic residential energy consumption pattern
Advancements in computational technologies for residential energy management have become widespread due to increased energy usage and peak demand in households. Controlled switching of appliances during peak periods for energy reduction is one of such solutions. However, this technique may constrict the consumer’s flexibility to utilize desired appliances to fulfil their needs. This study proposes a peak load control technique based on an end-use appliance prioritization and event detection algorithm. A noteworthy feature of the technique is the users’ preferred appliance (UPA), which provides the occupants with the flexibility to use any electric load to fulfill their needs at any time whether peak demand control has been initiated or not. Furthermore, to address the challenging issue around the uncertain and varying nature of the users’ energy usage pattern, an empirical bottom-up approach is adopted to characterize the consumers’ diverse end-use behavior. The results obtained show good performance in terms of peak demand and energy consumption reduction, with 3% to 20% and at least 14.05% in demand reduction for time of use (ToU) periods and energy savings, respectively. These offer a further perspective on demand side management, affording load users a new cost-saving energy usage pattern.