Effective scheduling of residential energy storage systems under dynamic pricing

We address the control of a residential energy storage system under dynamic pricing, for scenarios with and without local electricity generation, by combining a dynamic programming approach with real-time correction of predictions of load and generated power. We performed simulations using energy generation and consumption data for 64 residences in the Pecan Street Project, and a range of seasonal dynamic price tables. Our algorithm was more effective than other approaches in reducing electricity costs under most tariffs, especially when the amount of electricity generated locally is small.