A smart power grid transforms the traditional electric grid into a user-centric, intelligent power network. The cost-saving potential of smart homes is an excellent motivating factor to involve users in smart grid operations. To that end, this survey explores the contemporary cost-saving strategies for smart grids from the users' perspective. The study shows that optimization methods are the most popular cost-saving techniques reported in the literature. These methods are used to plan scheduling and power utilization schemes of household appliances, energy storages, renewables, and other energy generation devices. The survey shows that trading energy among neighborhoods is one of the effective methods for cost optimization. It also identifies the prediction methods that are used to forecast energy price, generation, and consumption profiles, which are required to optimize energy cost in advance. The contributions of this article are threefold. First, it discusses the computational methods reported in the literature with their significance and limitations. Second, it identifies the components and their characteristics that may reduce energy cost. Finally, it proposes a unified cost optimization framework and addresses the challenges that may influence the overall residential energy cost optimization problem in smart grids.

Additional Metadata
Keywords Cost optimization, Demand and response, Dynamic price, Energy storage, Microgrid, Renewables, Smart grid, Smart homes, Survey
Persistent URL dx.doi.org/10.1145/2897165
Journal ACM Computing Surveys
Alam, M.R. (Muhammad Raisul), St-Hilaire, M, & Kunz, T. (2016). Computational methods for residential energy cost optimization in smart grids: a survey. ACM Computing Surveys, 49(1). doi:10.1145/2897165