Heuristic scheduling of multiple smart home appliances: Utility planning perspective

Abstract

Electric utilities are increasingly incorporating Demand Side Management (DSM) approaches in their energy networks to help compensate for increased levels of uncertainty arising from renewable energy production. Demand Response (DR) is one such approach. DR aims to encourage shifts in residential load by using pricing signals and dynamic tariff mechanisms which are provided in real-time by the utility company. The goal is to shift energy consumption patterns to off-peak times and hence reduce the Peak-to-Average Ratio (PAR) of the daily electricity demand. In this paper, the effects of multiple households using a fast heuristic algorithm for scheduling smart appliances is simulated from a utility planning perspective. It explores the aggregated response of the de-centralized heuristic algorithms to events signaled by the utility, when the primary focus of each heuristic is upon minimization of end-user economic costs. The performance of the heuristic algorithm for DR events under normal and stringent conditions is explored under simulation. Results confirm that the aggregated demand can potentially respond to DR signals, although the choice of price signals plays a major role in the depth and nature of the response and requires further investigation.