A novel hybrid lexicographic-IGDT methodology for robust multi-objective solution of home energy management systems

Abstract

With the emergence of smart appliances and communication infrastructures, Home Energy Management Systems have gained importance to help home users to reduce their electricity bills. A Home Energy Management System is a tool able to optimally coordinate the different home assets such as controllable appliances, onsite generators and storage facilities, among others. Such kind of tools has become more complex with the appearance of dynamic pricing tariffs and novel appliances such as electric vehicles. In this context, scheduling tools must attain a high level of robustness against uncertainties as well as being able to consider the particular behaviour of unpredictable energy pricing and vehicle routines, while some complementary objectives like thermal comfort are not ignored. This paper addresses this issue by developing a novel hybrid robust-multi objective home energy management approach. The novel proposal is based on information gap decision theory and lexicographic optimization, which are combined in an original way to attain a scheduling plan immune against the negative effect of uncertainties, while the economy and comfort of the building are jointly considered. The developed mathematical formulation is Mixed Integer Linear Programming, employing advanced linearization techniques to overcome the problems arisen from nonlinear models. Its particular integer-linear structure makes the developed optimization problem easily tractable by conventional solvers and average machines. Also, further capabilities are explored such as the possibility of selling energy to the grid and the vehicle-to-home ability of electric vehicles. Extensive simulations are performed on a benchmark prosumer environment considering real time pricing and time-of-use tariffs. The result serves to prove that the developed methodology is able to deal with uncertainties whereas different objective functions are jointly accounted.