Modeling for Residential Electricity Optimization in Dynamic Pricing Environments

This paper proposes an energy scheduling model and optimization algorithms for residential electricity consumers who attempt to optimally schedule their electricity consumption, generation and storage in a dynamic pricing environment. We describe an optimization problem which integrates electric, thermodynamic, economic, comfort, and possibly environmental parameters. We present the algorithmic solution and provide simulations results, based on a robust optimization approach which minimizes the impact of stochastic input on the objective function. We argue that the scheduling problem is complex enough to be beyond the analytical capabilities of average residential customers. This result supports the need of scheduling controllers deployed as Home Energy Management Systems (HEMS).