Energy management is increasingly becoming an important issue in face of the penetration of renewable generation and the evolution to smart grids. Home energy management systems are aimed to make the integrated optimization of residential energy resources, taking into account energy prices and end-user's requirements. This paper addresses a residential scenario where energy resources are automatically managed to reduce the overall energy cost while considering a set of user-defined comfort preferences. These energy resources include the grid, shiftable appliances, thermostatic loads, static batteries, electric vehicles, and local energy production. The comfort specifications consist of the time slots where the shiftable appliances are preferred to operate and the temperature ranges desired for the thermostatically controlled loads. The conflicting objectives are addressed by a multi-objective genetic algorithm that aims to minimize the overall energy cost and the user's dissatisfaction. This paper proposes a set of novel operators that result in statistically significant improvements in terms of hypervolume values when compared to a recently proposed genetic algorithm customized to address this same scenario. These novel operators include a different population initialization, a greedy mutation, and two geometric crossovers. The effect of the proposed operators on the resulting allocation of energy resources is analyzed.

Dynamic tariffs are expected to become a relevant pricing scheme in the context of smart grids. In this framework, active management of residential loads can play an important role to optimize the usage of end-use energy resources while minimizing energy cost. This paper presents an evolutionary algorithm to optimize the integrated usage of multiple residential energy resources (local generation, shiftable loads, thermostatically controlled loads, and storage systems) considering a large set of management strategies. Customized solution encoding and operators are developed for different groups of loads. The multiobjective model considers as objective functions the minimization of the energy cost and the minimization of end-user's dissatisfaction associated with management strategies. Results have shown that significant savings can be achieved mainly through demand response actions implemented over thermostatically controlled loads. Savings are also dependent on the end-user's preferences and degree of willingness to accept automated control.