Ennio Grasso

Heuristic approaches for a domestic energy management system

Abstract In the context of smart buildings, house energy consumption plays a significant role in the energy system. Since there are different billing rates, the aim of the smart grid system is to minimize the overall energy cost by appropriately selecting the best starting times for each household appliance. Further, the scheduler should guarantee that the instant energy request does not overcome the maximum threshold which depends on the customers contract. Although the maximum threshold is a constant, the instant energy consumption may possibly exceed this value, whenever is available some renewable source such as, for instance, solar energy. In this work, we consider the problem of scheduling smart home appliances within a given time horizon respecting the threshold on the energy peak consumption with the objective of minimizing the overall cost related to the average energy consumption of each appliance at each time instant. A hybrid heuristic based on a mixed integer programming formulation of the problem and a particle swarm optimization approach are proposed. Computational results, applying these approaches to real life data are provided and discussed.