A Probabilistic Approach to Study the Load Variations in Aggregated Residential Load Patterns

The demand side in a power system has key importance in the evolving context of the energy systems. Exploitation of possible flexibilities of the customer's behavior is considered as an important option to promote demand response programmes and to achieve greater energy savings. For this purpose, the first action required is to augment availability of information about consumption patterns. The electricity consumption in a residential system is highly dependent on various types of uncertainties due to the diverse lifestyle of customers. Knowledge about the aggregated behavior of residential customers is very important for the system operator or aggregator to manage load and supply side flexibilities for economic operation of the system. In this paper, the effect of sampling time is evaluated for different residential load aggregations using probabilistic approach. A binomial probability distribution model is used to extract trends in increase or decrease in demand with respect to time evolution of a typical day. For each case study scenario, confidence intervals are calculated to assess the uncertainty and randomness in load variation trends. The findings of this study will lead towards better management of demand and supply side resources in a smart grid and especially for microgrid