Residential Load Variability and Diversity at Different Sampling Time and Aggregation Scales

The increasing use of large-scale intermittent distributed renewable energy resources on the electrical power system introduces uncertainties in both network planning and management. In addition to architectural changes to the power system, the applications of demand side response (DSR) also add a dimension of complexity - thereby converting the traditionally passive customers into active prosumers (customers that both produce and consume electricity). It has therefore become important to conduct detailed studies on system load profiles to uncover the nature of the system load. These studies could help distribution network operators (DNOs) to adopt relevant strategies that can accommodate new resources such as distributed generation and energy storage on the evolving distribution network and ensure updated design and management approaches. This paper investigates the relationship between both the system load diversity and variability when different customers are aggregated at different scales. Additionally, the implication of sampling time scales is investigated to capture its effect on load diversity and variability. The study looks at the diversity and variability that is observable from the viewpoint of higher power levels, when interconnecting different sized groupings of customers, at different sampling resolutions. The paper thus concludes that the per-customer capacity requirement of the network decreases as the size of customer groupings increases. The load variability also decreases as the aggregation level increases. For active network management, faster time scales are required at lower aggregation scales due to high load variability

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 microgrids